CN119313680B

CN119313680B - Glaucoma distinguishing and detecting method and system based on multi-mode fusion

Info

Publication number: CN119313680B
Application number: CN202411872145.0A
Authority: CN
Inventors: 刘雍建; 谢鲲; 周一; 文吉刚; 夏晓波
Original assignee: Hunan University
Current assignee: Hunan University
Priority date: 2024-12-18
Filing date: 2024-12-18
Publication date: 2025-03-11
Anticipated expiration: 2044-12-18
Also published as: CN119313680A

Abstract

The present invention discloses a glaucoma differentiation detection method and system based on multimodal fusion. The method relates to the technical field of glaucoma differentiation detection, and includes the following steps: collecting glaucoma differentiation detection data; analyzing glaucoma differentiation detection data; and comprehensively analyzing and optimizing the method for adjusting the quality accuracy of glaucoma differentiation detection data. The present invention collects and processes glaucoma differentiation detection data, analyzes the glaucoma differentiation detection data, obtains a glaucoma differentiation detection data integrity assessment index and a glaucoma differentiation detection data image quality assessment index, comprehensively analyzes and optimizes the method for adjusting the quality accuracy of glaucoma differentiation detection data, thereby improving the accuracy of the glaucoma differentiation detection method and system based on multimodal fusion, and solving the problem of insufficient accuracy of the glaucoma differentiation detection method and system based on multimodal fusion in the prior art.

Description

Glaucoma distinguishing and detecting method and system based on multi-mode fusion

Technical Field

The invention relates to the technical field of glaucoma distinguishing detection, in particular to a glaucoma distinguishing detection method and system based on multi-mode fusion.

Background

Glaucoma is a major cause of irreversible blindness worldwide, and is characterized by progressive retinal ganglion cell loss, structural changes of the optic nerve head and corresponding visual function defects, early detection and preventive treatment are critical for reducing vision loss caused by glaucoma, traditional glaucoma diagnosis mainly depends on subjective analysis of clinicians, such as observation of fundus images and OCT images, which requires a large number of experienced experts, single-mode data analysis often has insufficient reliability, the glaucoma diagnosis process requires combined analysis of functionality and structure, with algorithm optimization, cloud service upgrading and data volume accumulation, successful application of artificial intelligence technology based on deep learning in the fields of images, videos, voices and the like is focused on ophthalmic experts, and multimode fusion technology is expected to improve accuracy and efficiency of glaucoma diagnosis, is helpful for early discovery and treatment, and reduces risks of vision disorder and blindness.

The existing glaucoma distinguishing and detecting system based on multi-mode fusion is used for observing structural changes of retina and optic nerve head through multi-mode image technology, performing feature extraction and fusion on multi-mode image data through deep learning and artificial intelligence by using a deep learning algorithm, particularly a model based on a transducer, extracting features from an OCT volume by using a 3D transducer model by using a glaucoma grading method based on spatial relation multi-mode feature fusion, and fusing the features of fundus images.

The device and the method for glaucoma detection are disclosed in the patent publication No. CN106308738B, and comprise a table display mechanism, an under-table detection mechanism and an installation connecting piece for connecting the table display mechanism and the under-table detection mechanism, wherein the table display mechanism comprises a front shell, an image display, a camera monitor, a medical display and a camera monitor, wherein the image display, the camera monitor and the camera monitor are arranged on the front shell, the medical display is electrically connected with the image display, the camera monitor is electrically connected with the camera monitor, the image display and the camera monitor are arranged on the front shell through a first installation frame, and the medical display and the camera monitor are arranged on the front shell through a second installation frame.

The wearable detection method and device for glaucoma disease risk comprise the steps of S1 obtaining a user movement posture, photoelectric volume pulse wave signals and illumination information of a bracelet, S2 calculating heart rate, blood oxygen and blood pressure information through the photoelectric volume pulse wave signals, transmitting data information of the user movement posture, heart rate, blood oxygen, blood pressure and illumination to a glaucoma risk identification model, S3 integrating and analyzing the data information of the user movement posture, heart rate, blood oxygen, blood pressure and illumination by using the glaucoma risk identification model to obtain the probability of the user suffering from glaucoma disease, and enabling a patient to realize glaucoma disease risk detection in daily life without blood drawing and eye pressure examination in hospitals, so that glaucoma disease screening is effectively facilitated.

However, in the process of implementing the technical scheme of the embodiment of the application, the application discovers that the above technology has at least the following technical problems:

The method has the problems that the quality accuracy of the multi-mode fusion glaucoma distinguishing detection data is insufficient, in the storage and transmission process of the glaucoma distinguishing detection data, the glaucoma distinguishing detection data is incomplete due to deviation of compressed images or format conversion, noise and artifact in glaucoma image detection are increased, the quality of the multi-mode fusion glaucoma image is inaccurate, and the accuracy of a multi-mode fusion-based glaucoma distinguishing detection method and a multi-mode fusion-based glaucoma distinguishing detection system is insufficient.

Disclosure of Invention

The embodiment of the application solves the problem of insufficient accuracy of the glaucoma distinguishing detection method and system based on the multi-mode fusion in the prior art by providing the glaucoma distinguishing detection method and system based on the multi-mode fusion, and improves the accuracy of the glaucoma distinguishing detection method and system based on the multi-mode fusion.

The embodiment of the application provides a glaucoma distinguishing detection method based on multi-mode fusion, which comprises the following steps of collecting and processing glaucoma distinguishing detection data, analyzing the glaucoma distinguishing detection data to obtain a glaucoma distinguishing detection data integrity evaluation index and a glaucoma distinguishing detection data image quality evaluation index, comprehensively analyzing to obtain a glaucoma distinguishing detection data quality accuracy evaluation index, carrying out first threshold comparison analysis on the glaucoma distinguishing detection data integrity evaluation index and the glaucoma distinguishing detection data integrity evaluation index, optimizing and adjusting the glaucoma distinguishing detection data integrity method, carrying out second threshold comparison analysis on the glaucoma distinguishing detection data image quality evaluation index and the glaucoma distinguishing detection data image quality evaluation index, optimizing and adjusting the glaucoma distinguishing detection data image quality method, and carrying out comprehensive threshold comparison analysis on the glaucoma distinguishing detection data quality accuracy evaluation index and the glaucoma distinguishing detection data quality accuracy evaluation index, and optimizing and adjusting the glaucoma distinguishing detection data quality accuracy method.

Furthermore, the specific steps of collecting and processing the glaucoma distinguishing detection data comprise collecting glaucoma distinguishing detection original data through a sensor device, and cleaning and denoising the glaucoma distinguishing detection original data to obtain glaucoma distinguishing detection data, wherein the glaucoma distinguishing detection data comprises glaucoma distinguishing detection data integrity data and glaucoma distinguishing detection data image quality data.

The glaucoma distinguishing detection data integrity evaluation index comprises a glaucoma distinguishing detection data entropy value of a preset glaucoma distinguishing detection data integrity detection point, a run length of the preset glaucoma distinguishing detection data integrity detection point, a peak signal intensity maximum value of the preset glaucoma distinguishing detection data integrity detection point, a peak signal intensity minimum value of the preset glaucoma distinguishing detection data integrity detection point, image noise of the preset glaucoma distinguishing detection data integrity detection point, image artifact intensity of the preset glaucoma distinguishing detection data integrity detection point, an ambient illumination angle of the preset glaucoma distinguishing detection data integrity detection point, and an illumination measurement device.

The specific steps of obtaining the glaucoma distinguishing detection data image quality evaluation index include obtaining an image compression ratio of a preset glaucoma distinguishing detection data image quality detection point through image compression software, obtaining a run length of the preset glaucoma distinguishing detection data image quality detection point through image compression software, obtaining an image contrast of the preset glaucoma distinguishing detection data image quality detection point through image processing software, obtaining an image brightness of the preset glaucoma distinguishing detection data image quality detection point through image processing software, obtaining an image noise of the preset glaucoma distinguishing detection data image quality detection point through image processing software, obtaining an image artifact strength of the preset glaucoma distinguishing detection data image quality detection point through image processing software, wherein the glaucoma distinguishing detection data image quality data comprises the image compression ratio, the run length, the image contrast, the image brightness, the image noise and the image artifact strength, and obtaining the glaucoma distinguishing detection data image quality evaluation index according to glaucoma distinguishing detection data image quality data analysis.

The specific step of comprehensively analyzing and obtaining the glaucoma distinguishing detection data quality accuracy assessment index comprises the steps of obtaining the image resolution of a preset glaucoma distinguishing detection data quality accuracy detection point through image processing software, and obtaining the glaucoma distinguishing detection data quality accuracy assessment index through comprehensive analysis of the glaucoma distinguishing detection data integrity assessment index, the glaucoma distinguishing detection data image quality assessment index and the image resolution.

Furthermore, the method for optimally adjusting the integrity of the glaucoma distinguishing detection data comprises the specific steps that if the integrity evaluation index of the glaucoma distinguishing detection data is larger than or equal to the first threshold value of the integrity evaluation index of the glaucoma distinguishing detection data, the method for optimally adjusting the integrity of the glaucoma distinguishing detection data is not needed, and if the integrity evaluation index of the glaucoma distinguishing detection data is lower than the first threshold value of the integrity evaluation index of the glaucoma distinguishing detection data, the corresponding adjustment scheme in the glaucoma distinguishing detection database is matched through the difference value of the integrity evaluation index of the glaucoma distinguishing detection data and the first threshold value of the integrity evaluation index of the glaucoma distinguishing detection data.

Furthermore, the method for optimally adjusting the image quality of the glaucoma distinguishing detection data comprises the specific steps that if the image quality evaluation index of the glaucoma distinguishing detection data is lower than or equal to the second threshold value of the image quality evaluation index of the glaucoma distinguishing detection data, the method for optimally adjusting the image quality of the glaucoma distinguishing detection data is not needed, and if the image quality evaluation index of the glaucoma distinguishing detection data is greater than the second threshold value of the image quality evaluation index of the glaucoma distinguishing detection data, the corresponding adjustment scheme in the glaucoma distinguishing detection database is matched through the difference value between the image quality evaluation index of the glaucoma distinguishing detection data and the second threshold value of the image quality evaluation index of the glaucoma distinguishing detection data.

The method for optimizing and adjusting the quality accuracy of the glaucoma distinguishing detection data comprises the specific steps of extracting a glaucoma distinguishing detection data quality accuracy assessment index comprehensive threshold value from a glaucoma distinguishing detection database, comparing the glaucoma distinguishing detection data quality accuracy assessment index with the glaucoma distinguishing detection data quality accuracy assessment index comprehensive threshold value, and if the glaucoma distinguishing detection data quality accuracy assessment index is greater than or equal to the glaucoma distinguishing detection data quality accuracy assessment index comprehensive threshold value, not needing to optimally adjust the glaucoma distinguishing detection data quality accuracy.

Further, the method for optimizing and adjusting the quality accuracy of the glaucoma distinguishing detection data further comprises the step of matching an adjusting scheme corresponding to the glaucoma distinguishing detection database through the difference value between the quality accuracy evaluation index of the glaucoma distinguishing detection data and the quality accuracy evaluation index of the glaucoma distinguishing detection data if the quality accuracy evaluation index of the glaucoma distinguishing detection data is lower than the comprehensive threshold of the quality accuracy evaluation index of the glaucoma distinguishing detection data.

The embodiment of the application provides a glaucoma distinguishing detection system based on multi-mode fusion, which comprises a glaucoma distinguishing detection data acquisition module, a glaucoma distinguishing detection data analysis module, a comprehensive analysis module and a glaucoma distinguishing detection data quality accuracy adjustment method module, wherein the glaucoma distinguishing detection data acquisition module is used for acquiring and processing glaucoma distinguishing detection data, the glaucoma distinguishing detection data analysis module is used for analyzing glaucoma distinguishing detection data to obtain a glaucoma distinguishing detection data integrity evaluation index and a glaucoma distinguishing detection data image quality evaluation index, the comprehensive analysis module is used for comprehensively analyzing to obtain a glaucoma distinguishing detection data quality accuracy evaluation index, the glaucoma distinguishing detection data quality adjustment method module is used for comparing the glaucoma distinguishing detection data integrity evaluation index with a glaucoma distinguishing detection data integrity evaluation index through a first threshold value, the glaucoma distinguishing detection data integrity adjustment method is optimized, the glaucoma distinguishing detection data image quality evaluation index and the glaucoma distinguishing detection data image quality evaluation index through a second threshold value comparison analysis, the glaucoma distinguishing detection data quality adjustment method is optimized, the glaucoma distinguishing detection data quality accuracy evaluation index and the glaucoma distinguishing detection data quality accuracy evaluation index are compared with the glaucoma distinguishing detection data quality accuracy evaluation index through a comprehensive threshold value, and the glaucoma distinguishing detection data quality adjustment method is optimized.

One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:

1. The glaucoma distinguishing detection data is collected and processed, analysis is carried out on the glaucoma distinguishing detection data to obtain a glaucoma distinguishing detection data integrity evaluation index and a glaucoma distinguishing detection data image quality evaluation index, the method for comprehensively analyzing and optimizing and adjusting the glaucoma distinguishing detection data quality accuracy is used for gradually improving the image quality by repeatedly applying filtering operation through an iterative filter, filtering parameters can be adjusted according to the previous result in each iteration, the glaucoma distinguishing detection accuracy is improved, and the problems of insufficient glaucoma distinguishing detection methods and system accuracy based on multi-mode fusion in the prior art are solved;

2. The glaucoma distinguishing detection data is analyzed to obtain an integrity evaluation index of the glaucoma distinguishing detection data and an image quality evaluation index of the glaucoma distinguishing detection data, key features in the glaucoma distinguishing detection image are enhanced by using an edge retaining filter, image noise is reduced, noise is removed and important image features are reserved by using multi-scale analysis, and the quality of the glaucoma distinguishing detection image is improved;

3. The glaucoma distinguishing and detecting method and system based on multi-mode fusion are improved by comprehensively analyzing, optimizing and adjusting the accuracy of the glaucoma distinguishing and detecting data quality, optimizing parameters of a filter, retaining edges and details of images, and gradually improving the image quality by applying filtering operation for a plurality of times through an iterative filter, so that the accuracy of the glaucoma distinguishing and detecting image quality is improved, and the reliability of the glaucoma distinguishing and detecting method and system based on multi-mode fusion is further improved.

Drawings

Fig. 1 is a flowchart of a glaucoma distinguishing and detecting method based on multi-mode fusion according to an embodiment of the present application;

fig. 2 is a schematic diagram of an evaluation index function of accuracy of quality of glaucoma distinguishing detection data according to an embodiment of the present application;

fig. 3 is a schematic structural diagram of a glaucoma distinguishing and detecting system based on multi-mode fusion according to an embodiment of the present application.

Detailed Description

The embodiment of the application solves the problem of insufficient accuracy of the glaucoma distinguishing detection method and system based on the multi-mode fusion in the prior art by providing the glaucoma distinguishing detection method and system based on the multi-mode fusion, analyzes the glaucoma distinguishing detection data by collecting and processing the glaucoma distinguishing detection data to obtain the integrity evaluation index of the glaucoma distinguishing detection data and the image quality evaluation index of the glaucoma distinguishing detection data, comprehensively analyzes and optimally adjusts the accuracy of the quality of the glaucoma distinguishing detection data, and improves the accuracy of the glaucoma distinguishing detection method and system based on the multi-mode fusion.

The technical scheme of the embodiment of the application aims to solve the problems of insufficient accuracy of a glaucoma distinguishing and detecting method and a glaucoma distinguishing and detecting system based on multi-mode fusion, and the general thought is as follows:

The glaucoma distinguishing detection data is acquired and processed, the glaucoma distinguishing detection data is analyzed, so that the integrity evaluation index of the glaucoma distinguishing detection data and the image quality evaluation index of the glaucoma distinguishing detection data are obtained, and the accuracy of the glaucoma distinguishing detection data is comprehensively analyzed, optimized and adjusted, so that the accuracy of the glaucoma distinguishing detection method and the system based on multi-mode fusion is improved.

In order to better understand the above technical solutions, the following detailed description will refer to the accompanying drawings and specific embodiments.

As shown in fig. 1, the method for distinguishing and detecting glaucoma based on multi-mode fusion is applied to a distinguishing and detecting glaucoma system based on multi-mode fusion, and comprises the steps of collecting and processing distinguishing and detecting glaucoma data, analyzing the distinguishing and detecting glaucoma data to obtain an integrity evaluation index of distinguishing and detecting glaucoma data and an image quality evaluation index of distinguishing and detecting glaucoma data, comprehensively analyzing to obtain an accuracy evaluation index of distinguishing and detecting glaucoma data, comparing the integrity evaluation index of distinguishing and detecting glaucoma data with a first threshold value of the integrity evaluation index of distinguishing and detecting glaucoma data, optimally adjusting the integrity method of distinguishing and detecting glaucoma data, comparing the quality evaluation index of distinguishing and detecting glaucoma data with a second threshold value of the quality evaluation index of distinguishing and detecting glaucoma data, optimally adjusting the quality method of distinguishing and detecting glaucoma data, and comprehensively comparing the accuracy evaluation index of distinguishing and detecting glaucoma data with the accuracy evaluation index of distinguishing and detecting glaucoma data, and optimally adjusting the accuracy method of distinguishing and detecting glaucoma.

In this embodiment, the multimodal data includes text and image data, wherein if there is a missing text data, a missing value can be obtained by, for example, comparing the linear correlation between clinical data by using a Person coefficient, selecting a parameter having a larger correlation with the missing value from the obtained values to a linear filling model for filling, so as to obtain an estimated value of the missing value in the clinical data, specifically, when filling the data, about 3 minutes can be used to fill thousands of pieces of data. The clinical data filled therein is put into a corresponding feature extraction network to obtain a feature G, and corresponding feature extraction is performed on the feature extraction network from the RNFL deviation map, the RNFL thickness map, and the optic nerve cross section map in the OCT apparatus instrument to obtain features T, A and E, respectively. Specifically, the feature extraction is performed on the data of different modes, so that the data of different modes can be effectively extracted. And carrying out feature fusion on the clinical data feature G, RNFL, the deviation map feature T, RNFL, the thickness map feature A and the optic nerve cross section map feature E through corresponding super parameters. Specifically, the present step performs adaptive feature fusion by setting a corresponding super parameter for data of each modality. And performing feature inversion on the trained multi-mode fusion network. The pathological analysis of the primary open angle glaucoma and the primary closed angle glaucoma is carried out by means of a thermal graph. The step utilizes a trained multi-modal fusion network to visualize the characteristics of primary open angle glaucoma and primary closed angle glaucoma in a gradient descent manner.

Further, the specific steps of collecting and processing glaucoma distinguishing detection data include collecting glaucoma distinguishing detection original data through a sensor device, and cleaning and denoising the glaucoma distinguishing detection original data to obtain glaucoma distinguishing detection data, wherein the glaucoma distinguishing detection data comprise glaucoma distinguishing detection data integrity data and glaucoma distinguishing detection data image quality data.

The glaucoma distinguishing detection data integrity evaluation index comprises the specific steps of obtaining a glaucoma distinguishing detection data entropy value of a preset glaucoma distinguishing detection data integrity detection point through image processing software, obtaining a run length of the preset glaucoma distinguishing detection data integrity detection point through image compression software, obtaining a peak signal intensity maximum value of the preset glaucoma distinguishing detection data integrity detection point through image processing software, obtaining a peak signal intensity minimum value of the preset glaucoma distinguishing detection data integrity detection point through image processing software, obtaining image noise of the preset glaucoma distinguishing detection data integrity detection point through a noise analysis tool, obtaining image artifact intensity of the preset glaucoma distinguishing detection data integrity detection point through image processing software, obtaining an ambient illumination angle of the preset glaucoma distinguishing detection data integrity detection point through illumination measurement equipment, wherein the glaucoma distinguishing detection data integrity data comprises the glaucoma distinguishing detection data entropy value, the run length, the peak signal intensity maximum value, the peak signal intensity minimum value, the image noise, the image artifact intensity and the ambient illumination angle, and obtaining the glaucoma distinguishing detection data integrity evaluation index according to the glaucoma distinguishing detection data integrity data analysis.

In this embodiment, the specific method for obtaining the glaucoma distinguishing detection data integrity assessment index from this analysis is as follows:

;

;;

The preset glaucoma distinguishing detection data integrity detection points are numbered sequentially, Represent the firstThe number of glaucoma distinguishing detection data integrity detection points under the individual glaucoma distinguishing detection data integrity detection segments,,The total number of the glaucoma distinguishing detection data integrity detection points is represented.

Dividing the preset glaucoma distinguishing detection data integrity time into glaucoma distinguishing detection data integrity detection segments of the same time length,A number indicating the integrity of the glaucoma distinguishing test data segment,,A total number representing the glaucoma distinguishing detection data integrity detection segment;

Represent the first Glaucoma distinguishing detection data integrity assessment index for each glaucoma distinguishing detection data integrity detection point;

Represent the first Image noise correction coefficients under the glaucoma distinguishing detection data integrity detection points;

Represent the first Image artifact intensity correction coefficients at the glaucoma distinguishing and detecting data integrity detection points;

Represent the first The glaucoma distinguishes the ambient light angle correction coefficient under the detection point of the data integrity;

Represent the first Glaucoma distinguishing detection data entropy values under the individual glaucoma distinguishing detection data integrity detection points;

the glaucoma distinguishing detection data entropy threshold value is obtained from a glaucoma distinguishing detection database, and can be an average value of glaucoma distinguishing detection data entropy values under the condition of detecting point of integrity of the glaucoma distinguishing detection data preset in a historical database;

The run length standard value is a preset run length standard value obtained from a glaucoma distinguishing detection database, and can be a run length average value under a glaucoma distinguishing detection data integrity detection point preset from a historical database, in the glaucoma distinguishing detection data transmission process, the setting of the run length can influence the integrity of the glaucoma distinguishing detection data, and if the run length and the run length standard value deviate greatly, the glaucoma distinguishing detection data has errors in the transmission process, so that the integrity evaluation index of the data is influenced;

Represent the first Distinguishing the run length under the detection data integrity detection point by glaucoma;

Represent the first Frequency domain difference coefficients under the detection section of the integrity of the individual glaucoma distinguishing detection data;

Represent the first Distinguishing image noise under the detection data integrity detection point by glaucoma;

The image noise standard value is a preset image noise standard value obtained from a glaucoma distinguishing detection database, and can be an image noise average value under a detection point of the integrity of glaucoma distinguishing detection data preset from a historical database;

Represent the first Distinguishing the image artifact intensity under the detection data integrity detection point by the glaucoma;

The image artifact strength standard value is a preset image artifact strength standard value obtained from a glaucoma distinguishing detection database, and can be an image artifact strength average value under a glaucoma distinguishing detection data integrity detection point preset from a historical database;

Represent the first Distinguishing the ambient illumination angle under the detection data integrity detection point by glaucoma;

The environmental illumination angle standard value is obtained from a glaucoma distinguishing detection database, and can be an environmental illumination angle average value under a glaucoma distinguishing detection data integrity detection point preset from a historical database;

Represent the first Peak signal intensity maxima under individual glaucoma discrimination detection data integrity detection segments;

Represent the first Peak signal intensity minima under individual glaucoma discrimination detection data integrity detection segments;

The error peak signal intensity standard value is a preset error peak signal intensity standard value obtained from a glaucoma distinguishing detection database, and can be an error peak signal intensity average value under a glaucoma distinguishing detection data integrity detection section preset from a historical database, and e represents a natural constant;

influencing weight factors for entropy values of preset glaucoma distinguishing detection data obtained from a glaucoma distinguishing detection database;

influencing the weight factor for a preset run length obtained from a glaucoma distinguishing detection database;

The weight factor is influenced by a preset frequency domain difference coefficient obtained from a glaucoma distinguishing detection database;

a preset image noise correction factor obtained from a glaucoma distinguishing detection database;

correcting factors for preset image artifact intensities acquired from a glaucoma distinguishing detection database;

And correcting factors for the preset environmental illumination angles obtained from the glaucoma distinguishing detection database.

The preset glaucoma distinguishing detection data entropy value influence weight factor, the preset run length influence weight factor and the preset frequency domain difference coefficient influence weight factor respectively represent the values of the influence degree of the glaucoma distinguishing detection data entropy value, the run length and the frequency domain difference coefficient on the glaucoma distinguishing detection data integrity assessment index, and represent the proportion of the glaucoma distinguishing detection data entropy value, the run length and the frequency domain difference coefficient in the glaucoma distinguishing detection data integrity assessment index.

The preset glaucoma distinguishing detection data entropy value influence weight factor, the preset run length influence weight factor and the preset frequency domain difference coefficient influence weight factor are obtained through a mapping relation, for example, a mapping set of the glaucoma distinguishing detection data entropy value, the run length and the frequency domain difference coefficient and the corresponding weight is respectively established through the relation between the glaucoma distinguishing detection data entropy value, the run length and the frequency domain difference coefficient in historical data and the data updating frequency, and the corresponding preset glaucoma distinguishing detection data entropy value influence weight factor, the preset run length influence weight factor and the preset frequency domain difference coefficient influence weight factor in the mapping set are obtained through inputting the real-time glaucoma distinguishing detection data entropy value, the run length and the frequency domain difference coefficient.

The preset image noise correction factor, the preset image artifact intensity correction factor and the preset ambient light angle correction factor respectively represent the numerical values of the influence degree of the image noise, the image artifact intensity and the ambient light angle on the glaucoma distinguishing detection data integrity assessment index, and represent the proportion of the image noise, the image artifact intensity and the ambient light angle in the glaucoma distinguishing detection data integrity assessment index.

The preset image noise correction factor, the preset image artifact intensity correction factor and the preset environment illumination angle correction factor are obtained through mapping relations, for example, mapping sets of the image noise, the image artifact intensity, the environment illumination angle and the corresponding weight are respectively established through relations among the image noise, the image artifact intensity, the environment illumination angle and the illumination intensity in historical data, and the preset image noise correction factor, the preset image artifact intensity correction factor and the preset environment illumination angle correction factor which correspond to the mapping sets are obtained through inputting the real-time image noise, the image artifact intensity and the environment illumination angle.

The larger the entropy of the glaucoma distinguishing detection data is, the more random noise exists in the image, the larger the image noise is, the maximum value and the minimum value of the peak signal intensity reflect the brightness range in the image, the higher the image contrast is related to the image contrast, the larger the square of the difference between the maximum value of the peak signal intensity and the maximum value of the peak signal intensity is, the image noise and the image artifact intensity are factors influencing the image quality, the image noise is increased, the image artifact intensity is more obvious, the noise level of the image is increased due to the existence of the image artifact, the larger the image artifact intensity is, the brightness and the contrast of the image are influenced by the ambient illumination angle, the image detail visibility is improved due to the fact that the deviation between the ambient illumination angle and the ambient standard value is smaller, the relationship between the maximum value and the minimum value of the peak signal intensity and the image noise is dependent on the brightness and the contrast of the image noise, the accuracy of the peak signal intensity is influenced due to the fact that the ambient illumination angle influences the noise level of the image, and the ambient illumination angle standard value deviation is larger, reflection and shadow in the image are caused, so that the noise is increased, and the image noise is larger.

The glaucoma distinguishing detection data entropy reflects the complexity and information quantity of the glaucoma distinguishing detection image, has negative correlation with the glaucoma distinguishing detection data integrity evaluation index, and the larger the absolute value of the glaucoma distinguishing detection data entropy and the glaucoma distinguishing detection data entropy standard value difference is, the smaller the glaucoma distinguishing detection data integrity evaluation index is; the run length reduces the data volume by continuously the same pixel value length, has negative correlation with the glaucoma distinguishing detection data integrity assessment index, the larger the absolute value of the run length and the run length standard value difference is, the smaller the glaucoma distinguishing detection data integrity assessment index is, the peak signal intensity maximum value and the minimum value reflect the brightness range in the image, the image with high contrast generally has larger peak signal intensity difference, has negative correlation with the glaucoma distinguishing detection data integrity assessment index, the smaller the square of the peak signal intensity maximum value and the peak signal intensity minimum value difference is, the larger the glaucoma distinguishing detection data integrity assessment index is, the square of the image noise and the image noise standard value difference is, the details of the image can be covered, the glaucoma distinguishing detection data integrity assessment index is smaller, the square of the image artifact intensity and the image artifact intensity standard value difference is in negative correlation with the glaucoma distinguishing detection data integrity assessment index is, the larger the square of the image artifact intensity and the image artifact intensity standard value is, the interference is larger, and the correct interpretation of the image content is carried out, the smaller the glaucoma distinguishing detection data integrity evaluation index, the negative correlation exists between the square of the standard value difference between the ambient illumination angle and the glaucoma distinguishing detection data integrity evaluation index, and the larger the square of the standard value difference between the ambient illumination angle and the ambient illumination angle, the image distortion is caused, and the smaller the glaucoma distinguishing detection data integrity evaluation index is.

The specific steps of obtaining the glaucoma distinguishing detection data image quality evaluation index comprise obtaining an image compression ratio of a preset glaucoma distinguishing detection data image quality detection point through image compression software, obtaining a run length of the preset glaucoma distinguishing detection data image quality detection point through image compression software, obtaining an image contrast of the preset glaucoma distinguishing detection data image quality detection point through image processing software, obtaining an image brightness of the preset glaucoma distinguishing detection data image quality detection point through image processing software, obtaining an image noise of the preset glaucoma distinguishing detection data image quality detection point through image processing software, obtaining an image artifact intensity of the preset glaucoma distinguishing detection data image quality detection point through image processing software, wherein the glaucoma distinguishing detection data image quality data comprises the image compression ratio, the run length, the image contrast, the image brightness, the image noise and the image artifact intensity, and obtaining the glaucoma distinguishing detection data image quality evaluation index according to glaucoma distinguishing detection data image quality data analysis.

In this embodiment, the specific method for obtaining the glaucoma distinguishing detection data image quality assessment index by this analysis is as follows:

;

;;

;

The image quality detection points of the preset glaucoma distinguishing detection data are numbered in sequence, A number indicating the glaucoma distinguishing detection data image quality detection point,,A number total number indicating the glaucoma distinguishing detection data image quality detection point;

Represent the first Glaucoma distinguishing detection data image quality assessment indices for the individual glaucoma distinguishing detection data image quality detection points;

Represent the first The glaucoma distinguishes the image compression ratio under the detection point of the image quality of the detection data;

the standard value of the image compression ratio is obtained from a glaucoma distinguishing detection database, and can be an average value of the image compression ratio under the detection point of the integrity of glaucoma distinguishing detection data preset from a historical database;

Represent the first Distinguishing the run length of the detected data under the image quality detection point by glaucoma;

the standard value of the run length is shown, and can be a preset run length standard value obtained from a glaucoma distinguishing detection database, which can be a run length average value under a preset glaucoma distinguishing detection data image quality detection point in a historical database, if the run length and the run length standard value deviate greatly, the glaucoma distinguishing detection data has errors in the transmission process, so that the image quality is affected;

Represent the first Distinguishing an image noise influence coefficient under the detection point of the image quality of the detection data by the glaucoma;

Represent the first Distinguishing an image artifact intensity influence coefficient under the detection data image quality detection point by glaucoma;

Represent the first Distinguishing image structure similarity coefficients under detection points of image quality of detection data by glaucoma;

Represent the first Distinguishing image noise under the detection point of the image quality of the detection data by glaucoma;

The image noise standard value is a preset image noise standard value obtained from a glaucoma distinguishing detection database, and can be an image noise average value under a preset glaucoma distinguishing detection data image quality detection point from a historical database;

Represent the first Distinguishing the image artifact intensity under the detection point of the image quality of the detection data by the glaucoma;

the image artifact strength standard value is a preset image artifact strength standard value obtained from a glaucoma distinguishing detection database, and can be an image artifact strength average value under a glaucoma distinguishing detection data image quality detection point preset from a historical database;

Represent the first Distinguishing the image contrast under the detection points of the image quality of the detection data by glaucoma;

Represent the first Distinguishing the image brightness under the detection point of the image quality of the detection data by the glaucoma;

The image contrast standard value is a preset image contrast standard value obtained from a glaucoma distinguishing detection database, and can be an image contrast average value under a preset glaucoma distinguishing detection data image quality detection point from a historical database;

the representative image brightness standard value is a preset image brightness standard value obtained from a glaucoma distinguishing detection database, and can be an image brightness average value under a preset glaucoma distinguishing detection data image quality detection point from a historical database;

Affecting the weight factor for a preset image compression ratio obtained from the glaucoma distinguishing detection database;

For a preset runlength influence factor obtained from the glaucoma distinguishing detection database, representing the proportion of the runlength in the image quality evaluation index of the glaucoma distinguishing detection data;

Influencing weight factors for preset image structure similarity coefficients acquired from a glaucoma distinguishing detection database;

Representing the proportion of image noise in the image quality evaluation index of the glaucoma distinguishing detection data for the preset image noise influence factor obtained from the glaucoma distinguishing detection database;

the preset image artifact intensity influence factor obtained from the glaucoma distinguishing detection database represents the proportion of the image artifact intensity in the image quality evaluation index of the glaucoma distinguishing detection data.

The preset image compression ratio influence weight factor, the preset run length influence factor and the preset image structure similarity coefficient influence weight factor respectively represent values of the influence degree of the image compression ratio, the run length and the image structure similarity coefficient on the glaucoma distinguishing detection data image quality assessment index, and represent the proportion of the image compression ratio, the run length and the image structure similarity coefficient in the glaucoma distinguishing detection data image quality assessment index.

The preset image compression ratio influence weight factor, the preset run length influence factor and the preset image structure similarity coefficient influence weight factor are obtained through mapping relations, for example, a mapping set of the image compression ratio, the run length and the image structure similarity coefficient and corresponding weights of the image compression ratio, the run length and the image structure similarity coefficient is respectively established through relations among the image compression ratio, the run length and the image structure similarity coefficient and the image resolution in historical data, and the corresponding preset image compression ratio influence weight factor, the preset run length influence weight factor and the preset image structure similarity coefficient influence weight factor in the mapping set are obtained through inputting the real-time image compression ratio, the run length and the image structure similarity coefficient.

The preset image noise influence factor and the preset image artifact intensity influence factor respectively represent values of the influence degree of the image noise and the image artifact intensity on the glaucoma distinguishing detection data image quality evaluation index, and represent the proportion of the image noise and the image artifact intensity in the glaucoma distinguishing detection data image quality evaluation index.

The preset image noise influence factor and the preset image artifact intensity influence factor are obtained through a mapping relation, for example, a mapping set of the image noise and the image artifact intensity and the corresponding weight of the image noise and the image artifact intensity is respectively established through the relation between the image noise and the image artifact intensity in the historical data and the filter cut-off frequency, and the preset image noise influence factor and the preset image artifact intensity influence factor corresponding to the mapping set are obtained through inputting the real-time image noise and the image artifact intensity.

The image compression ratio is increased, the image quality is reduced, because more image information is lost, the longer the run length is, the more continuous pixel values are represented to be the same, the image compression ratio is increased, the image noise is increased, because the additional error is introduced in the image compression process of the glaucoma distinguishing detection data, the image compression ratio is increased, the image artifact strength is increased, because the additional artifact is possibly introduced in the image compression process of the glaucoma distinguishing detection data, the larger the deviation between the image contrast and the standard value of the image contrast is, the more obvious the image noise is, the larger the image noise is, the visibility of the image artifact is influenced by adjusting the image brightness, because the image artifact becomes more obvious or more concealed along with the change of the image brightness, the image brightness is increased, the image brightness is reduced, and the image artifact strength is increased.

The larger the absolute value of the image compression ratio and the image compression ratio difference is, the larger the absolute value of the image compression ratio and the image quality evaluation index of glaucoma distinguishing detection data is, and therefore, some image information is lost, so that the image quality of glaucoma distinguishing detection data is affected, the larger the image quality evaluation index of glaucoma distinguishing detection data is, the positive correlation exists between the absolute value of the run length and the run length standard value difference and the image quality evaluation index of glaucoma distinguishing detection data, the lower the image compression efficiency of glaucoma distinguishing detection data is, the larger the image quality evaluation index of glaucoma distinguishing detection data is, the square of the image contrast and the image contrast standard value difference is, the positive correlation exists between the square of the image contrast and the image quality evaluation index of glaucoma distinguishing detection data, the larger the image contrast and the image contrast standard value difference is, the glaucoma distinguishing detection data is easy to be exposed, the image quality of glaucoma distinguishing detection data is affected, the image quality of glaucoma distinguishing detection data is easy to be evaluated, the square of the image brightness and the glaucoma distinguishing detection data is easy to be evaluated, the square of the image noise of glaucoma distinguishing detection data is easy to be affected, the square of the image noise of glaucoma distinguishing detection data is large, and the square of the image noise of glaucoma distinguishing detection data is easy to be evaluated, and the square of the image noise is large. The positive correlation exists between the square of the standard value difference of the image artifact intensity and the image quality evaluation index of the glaucoma distinguishing detection data, and the larger the square of the standard value difference of the image artifact intensity and the image artifact intensity is, the larger the image quality is reduced, and the larger the image quality evaluation index of the glaucoma distinguishing detection data is.

The specific steps of comprehensively analyzing and obtaining the glaucoma distinguishing detection data quality accuracy assessment index include obtaining the image resolution of a preset glaucoma distinguishing detection data quality accuracy detection point through image processing software, and obtaining the glaucoma distinguishing detection data quality accuracy assessment index through comprehensive analysis of the glaucoma distinguishing detection data integrity assessment index, the glaucoma distinguishing detection data image quality assessment index and the image resolution.

In this embodiment, the specific method for obtaining the glaucoma distinguishing detection data quality accuracy assessment index by this analysis is as follows:

;

The detection points with the accuracy of the quality of the preset glaucoma distinguishing detection data are sequentially numbered, A number indicating the accuracy of the quality of glaucoma distinguishing test data,,The total number of numbers of detection points for distinguishing the quality of the detection data of the glaucoma is represented;

an evaluation index indicating the accuracy of the quality of the glaucoma distinguishing detection data;

Represent the first The glaucoma distinguishes the image resolution under the detection point of the quality accuracy of the detection data;

the representation image resolution threshold is a preset image resolution threshold obtained from a glaucoma distinguishing detection database, and can be an average value of image resolutions under a detection point of preset glaucoma distinguishing detection data quality accuracy from a historical database;

the method comprises the steps of evaluating an index influence weight factor for the integrity of preset glaucoma distinguishing detection data obtained from a glaucoma distinguishing detection database;

influencing weight factors for preset image resolution acquired from a glaucoma distinguishing detection database;

the weighting factor is influenced by an image quality evaluation index for preset glaucoma distinguishing detection data acquired from a glaucoma distinguishing detection database.

The preset glaucoma distinguishing detection data integrity evaluation index influence weight factor, the preset image resolution influence weight factor and the preset glaucoma distinguishing detection data image quality evaluation index influence weight factor respectively represent values of the glaucoma distinguishing detection data integrity evaluation index, the image resolution and the influence degree of the glaucoma distinguishing detection data image quality evaluation index on the glaucoma distinguishing detection data quality accuracy evaluation index, and represent the proportion of the glaucoma distinguishing detection data integrity evaluation index, the image resolution and the glaucoma distinguishing detection data image quality evaluation index in the glaucoma distinguishing detection data quality accuracy evaluation index.

The preset glaucoma distinguishing detection data integrity evaluation index influence weight factor, the preset image resolution influence weight factor and the preset glaucoma distinguishing detection data image quality evaluation index influence weight factor are obtained through a mapping relation, for example, a mapping set of the glaucoma distinguishing detection data integrity evaluation index, the image resolution and the glaucoma distinguishing detection data image quality evaluation index and corresponding weights thereof is respectively established through the relation between the glaucoma distinguishing detection data integrity evaluation index, the image resolution and the glaucoma distinguishing detection data image quality evaluation index and the signal to noise ratio in historical data, and the preset glaucoma distinguishing detection data integrity evaluation index influence weight factor, the preset image resolution influence weight factor and the preset glaucoma distinguishing detection data image quality evaluation index influence weight factor corresponding to the mapping set are obtained through input of the real-time glaucoma distinguishing detection data integrity evaluation index, the real-time glaucoma distinguishing detection data image quality evaluation index.

Table 1 is an exemplary table of glaucoma distinguishing detection data quality accuracy assessment index, the exemplary parameters in table 1 are illustrated by taking only one parameter under the glaucoma distinguishing detection data quality accuracy detection point, the weight factorsSet to 0.4, weight factorSet to 0.3, weight factorThe temperature of the mixture is set to be 0.3,The image resolution threshold is 2 and table 1 is shown below.

Table 1 glaucoma distinguishing test data quality accuracy assessment index example table:

;

The image resolution refers to the number of pixels in an image, the definition and details of the image are affected, the higher the image resolution is, the more details are contained in the image, more accurate and detailed image information is provided, the higher the integrity evaluation index of glaucoma distinguishing detection data is, the higher the image resolution is, the higher the structural similarity, peak signal-to-noise ratio and the like of the image are, the smaller the image quality evaluation index of glaucoma distinguishing detection data is, the integrity evaluation index of glaucoma distinguishing detection data reflects the integrity and reliability of glaucoma detection data, and the higher the integrity evaluation index of glaucoma distinguishing detection data is, the smaller the image quality evaluation index of glaucoma distinguishing detection data is.

As can be seen from table 1, the greater the glaucoma distinguishing detection data integrity assessment index, the more complete the information of the glaucoma distinguishing detection data is provided, the greater the glaucoma distinguishing detection data integrity assessment index has positive correlation with the glaucoma distinguishing detection data quality accuracy assessment index, the greater the glaucoma distinguishing detection data quality accuracy assessment index is, the higher the image resolution is, the more detailed optic nerve and retina structure information can be provided, thereby possibly improving the accuracy of the glaucoma detection data, the greater the image resolution has positive correlation with the glaucoma distinguishing detection data quality assessment index, the greater the glaucoma distinguishing detection data quality accuracy assessment index is, the image quality influence is on the accuracy of glaucoma diagnosis, the greater the glaucoma distinguishing detection data image quality assessment index has negative correlation with the glaucoma distinguishing detection data quality accuracy assessment index, and the smaller the glaucoma distinguishing detection data quality accuracy assessment index is.

As shown in FIG. 2, an index function diagram for evaluating the accuracy of quality of glaucoma distinguishing detection data according to an embodiment of the present application is shown, wherein x is the positive half axis of the abscissa, y is the positive half axis of the ordinate, and the weight factorSet to 0.4, weight factorSet to 0.3, weight factorThe temperature of the mixture is set to be 0.3,The image resolution threshold is 2.

The curve a shows that if the image resolution is set to be a fixed value 1, the glaucoma distinguishing detection data image quality evaluation index is set to be a fixed value 1, the glaucoma distinguishing detection data integrity evaluation index is set to be x, the glaucoma distinguishing detection data quality accuracy evaluation index is set to be y, and the glaucoma distinguishing detection data quality accuracy evaluation index rises along with the rising of the glaucoma distinguishing detection data integrity evaluation index.

Further, the method for optimally adjusting the integrity of the glaucoma distinguishing detection data comprises the specific steps that if the integrity evaluation index of the glaucoma distinguishing detection data is larger than or equal to the first threshold value of the integrity evaluation index of the glaucoma distinguishing detection data, the method for optimally adjusting the integrity of the glaucoma distinguishing detection data is not needed, and if the integrity evaluation index of the glaucoma distinguishing detection data is lower than the first threshold value of the integrity evaluation index of the glaucoma distinguishing detection data, the corresponding adjustment scheme in the glaucoma distinguishing detection database is matched through the difference value between the integrity evaluation index of the glaucoma distinguishing detection data and the first threshold value of the integrity evaluation index of the glaucoma distinguishing detection data.

In the embodiment, assuming that the glaucoma distinguishing detection data integrity assessment index is 1, the first threshold value of the glaucoma distinguishing detection data integrity assessment index obtained from the glaucoma distinguishing detection database is 2, and the corresponding difference value is-1, matching is performed from the glaucoma distinguishing detection database to obtain an adjustment scheme corresponding to the situation that the difference value of the glaucoma distinguishing detection data integrity assessment index and the glaucoma distinguishing detection data integrity assessment index is-1, wherein the adjustment scheme is that high-frequency information, such as edges and details, in an image can be enhanced through a high-pass filter, such as a Sobel operator, an edge feature in the image can be enhanced through an edge retaining filter, interference to other parts of the image can be reduced as much as possible, and the key features in the image, such as edges, corner points, texture analysis, colors and the like, can be achieved through an edge retaining filter, such as Canny edge detection, so as to improve the glaucoma distinguishing detection data integrity assessment index.

Further, the specific steps of the method for optimally adjusting the image quality of the glaucoma distinguishing detection data are that if the image quality evaluation index of the glaucoma distinguishing detection data is lower than or equal to the second threshold value of the image quality evaluation index of the glaucoma distinguishing detection data, the method for optimally adjusting the image quality of the glaucoma distinguishing detection data is not needed, and if the image quality evaluation index of the glaucoma distinguishing detection data is greater than the second threshold value of the image quality evaluation index of the glaucoma distinguishing detection data, the corresponding adjusting scheme in the glaucoma distinguishing detection database is matched through the difference value between the image quality evaluation index of the glaucoma distinguishing detection data and the second threshold value of the image quality evaluation index of the glaucoma distinguishing detection data.

In this embodiment, assuming that the glaucoma distinguishing detection data image quality evaluation index is 3, and the second threshold value of the glaucoma distinguishing detection data image quality evaluation index obtained from the glaucoma distinguishing detection database is 1, and the corresponding difference value is 2, matching is performed from the glaucoma distinguishing detection database to obtain an adjustment scheme corresponding to the case that the difference value between the glaucoma distinguishing detection data image quality evaluation index and the glaucoma distinguishing detection data image quality evaluation index is 2, the adjustment scheme is that the image is subjected to wavelet transformation, the wavelet transformation is a method for decomposing the image into different scale and position components, in this example, the wavelet transformation is selected to decompose the image into an approximation coefficient and a detail coefficient, the approximation coefficient represents a low-frequency component of the image, the detail coefficient represents a high-frequency component of the image, in this example, the decomposition into a second level is selected, in order to remove noise, a threshold value can be applied to the detail coefficient, in this example, the absolute value of all detail coefficients is limited to be within 0.2, meaning that if the absolute value is larger than 0.2, the image is subjected to inverse transformation, the image is reconstructed after the image is subjected to the inverse transformation, and the detail coefficient is reduced to the image is reduced to the inverse transformation, and the image is subjected to the inverse transformation, and the image is reconstructed, if the absolute value is greater than 0.2.

The method for optimizing and adjusting the quality accuracy of the glaucoma distinguishing detection data comprises the specific steps of extracting a glaucoma distinguishing detection data quality accuracy assessment index comprehensive threshold value from a glaucoma distinguishing detection database, comparing the glaucoma distinguishing detection data quality accuracy assessment index with the glaucoma distinguishing detection data quality accuracy assessment index comprehensive threshold value, and if the glaucoma distinguishing detection data quality accuracy assessment index is greater than or equal to the glaucoma distinguishing detection data quality accuracy assessment index comprehensive threshold value, not needing to optimize and adjust the glaucoma distinguishing detection data quality accuracy.

Further, the method for optimizing and adjusting the quality accuracy of the glaucoma distinguishing detection data further comprises the step of matching an adjustment scheme corresponding to the glaucoma distinguishing detection database through the difference value between the quality accuracy evaluation index of the glaucoma distinguishing detection data and the quality accuracy evaluation index of the glaucoma distinguishing detection data if the quality accuracy evaluation index of the glaucoma distinguishing detection data is lower than the comprehensive threshold of the quality accuracy evaluation index of the glaucoma distinguishing detection data.

In the embodiment, assuming that the glaucoma distinguishing detection data quality accuracy assessment index is 2.5, the glaucoma distinguishing detection data quality accuracy assessment index comprehensive threshold obtained from a glaucoma distinguishing detection database is 3, and the corresponding difference value is-0.5, matching from the glaucoma distinguishing detection database to obtain a corresponding adjustment scheme when the difference value between the glaucoma distinguishing detection data quality accuracy assessment index and the glaucoma distinguishing detection data quality accuracy assessment index comprehensive threshold is-0.5, wherein the adjustment scheme is that an initial filter parameter is selected, for example, the standard deviation of a Gaussian filter is 1.0, the Gaussian filter is applied to an image, noise is removed, the filtering effect is assessed through visual inspection or quantitative indexes such as signal to noise ratio, structural similarity index and the like, the filter parameter is adjusted according to the filtering effect, for example, if the image is still more noisy, the standard deviation of the Gaussian filter can be increased, and the above steps are repeated until the glaucoma distinguishing detection data quality accuracy assessment index is larger than or equal to the glaucoma distinguishing detection data quality accuracy assessment index comprehensive threshold, and the above process reaches a preset number of times, namely, the glaucoma detection parameter including noise is selected, the first iteration is that the Gaussian filter is applied for 1, the image has a fuzzy filtering effect, and the filtering effect is still found out that the noise is still has a certain detail; a second iteration, in which the standard deviation of the Gaussian filter is increased to 1.5 according to the result of the first iteration, the filter is reapplied, the noise is found to be reduced, but the edges and details are still not clear enough, and a third iteration, in which the decision is made to try to use the bilateral filter according to the result of the second iteration, so as to better preserve the edges and details, and continuing iteration, namely adjusting the parameters of the filter each time according to the previous result until the evaluation index reaches or exceeds the comprehensive threshold or reaches a preset iteration number.

The glaucoma distinguishing detection system based on the multi-mode fusion provided by the embodiment of the application comprises a glaucoma distinguishing detection data acquisition module, a glaucoma distinguishing detection data analysis module, a comprehensive analysis module and a glaucoma distinguishing detection data quality accuracy adjustment method module, wherein the glaucoma distinguishing detection data acquisition module is used for acquiring and processing glaucoma distinguishing detection data, the glaucoma distinguishing detection data analysis module is used for analyzing glaucoma distinguishing detection data to obtain a glaucoma distinguishing detection data integrity evaluation index and a glaucoma distinguishing detection data image quality evaluation index, the comprehensive analysis module is used for comprehensively analyzing glaucoma distinguishing detection data quality accuracy evaluation index, the glaucoma distinguishing detection data quality accuracy adjustment method module is used for comparing the glaucoma distinguishing detection data integrity evaluation index with a glaucoma distinguishing detection data integrity evaluation index by a first threshold value, the glaucoma distinguishing detection data integrity adjustment method is used for optimizing and adjusting, the glaucoma distinguishing detection data image quality evaluation index and a glaucoma distinguishing detection data image quality evaluation index by a second threshold value comparison analysis, the glaucoma distinguishing detection data quality adjustment method is optimized, and the glaucoma distinguishing detection data quality accuracy evaluation data quality accuracy and the glaucoma distinguishing detection data quality accuracy evaluation index is optimized and adjusted by the comprehensive analysis method.

It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.

It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims

1. The glaucoma distinguishing and detecting method based on multi-mode fusion is characterized by comprising the following steps of:

collecting and processing glaucoma distinguishing detection data;

Analyzing the glaucoma distinguishing detection data to obtain an integrity evaluation index of the glaucoma distinguishing detection data and an image quality evaluation index of the glaucoma distinguishing detection data;

comprehensively analyzing to obtain glaucoma distinguishing detection data quality accuracy assessment index;

comparing and analyzing the glaucoma distinguishing detection data integrity evaluation index with a first threshold value of the glaucoma distinguishing detection data integrity evaluation index, and optimizing and adjusting the glaucoma distinguishing detection data integrity method;

Comparing and analyzing the glaucoma distinguishing detection data image quality evaluation index with a second threshold value of the glaucoma distinguishing detection data image quality evaluation index, and optimizing and adjusting the glaucoma distinguishing detection data image quality method;

comparing and analyzing the glaucoma distinguishing detection data quality accuracy assessment index with a comprehensive threshold of the glaucoma distinguishing detection data quality accuracy assessment index, and optimizing and adjusting the glaucoma distinguishing detection data quality accuracy method;

the method for optimizing and adjusting the integrity of glaucoma distinguishing detection data comprises the following specific steps of:

If the glaucoma distinguishing detection data integrity assessment index is greater than or equal to the glaucoma distinguishing detection data integrity assessment index first threshold, the glaucoma distinguishing detection data integrity method does not need to be optimized and adjusted;

If the glaucoma distinguishing detection data integrity evaluation index is lower than the glaucoma distinguishing detection data integrity evaluation index first threshold, matching the corresponding adjustment scheme in the glaucoma distinguishing detection database through the difference value between the glaucoma distinguishing detection data integrity evaluation index and the glaucoma distinguishing detection data integrity evaluation index first threshold;

the specific adjustment scheme is that the high-frequency information in the image of the glaucoma distinguishing detection data is enhanced by a high-pass filter, the edge characteristic in the image is enhanced by an edge retaining filter, the key characteristic in the glaucoma distinguishing detection image is enhanced by the edge retaining filter;

The method for optimizing and adjusting the glaucoma distinguishing detection data image quality comprises the following specific steps of:

If the glaucoma distinguishing detection data image quality evaluation index is lower than or equal to the glaucoma distinguishing detection data image quality evaluation index second threshold, the glaucoma distinguishing detection data image quality optimization adjustment method is not needed;

If the glaucoma distinguishing detection data image quality evaluation index is larger than the glaucoma distinguishing detection data image quality evaluation index second threshold, matching the corresponding adjustment scheme in the glaucoma distinguishing detection database through the difference value of the glaucoma distinguishing detection data image quality evaluation index and the glaucoma distinguishing detection data image quality evaluation index second threshold;

The specific adjustment scheme is that wavelet transformation is carried out on the image of glaucoma distinguishing detection data, noise is removed, and image characteristics are reserved;

The method for optimizing and adjusting the accuracy of the quality of glaucoma distinguishing detection data further comprises the following steps:

If the glaucoma distinguishing detection data quality accuracy evaluation index is lower than the glaucoma distinguishing detection data quality accuracy evaluation index comprehensive threshold, matching an adjustment scheme corresponding to the glaucoma distinguishing detection database through the difference value between the glaucoma distinguishing detection data quality accuracy evaluation index and the glaucoma distinguishing detection data quality accuracy evaluation index comprehensive threshold;

The specific adjustment scheme is that iterative filtering operation is applied to the glaucoma distinguishing detection data image through an iterative filter, initial filter parameters are selected, the filtering effect is estimated through visual inspection or quantitative indexes, the filter parameters are adjusted according to the filtering effect, and the iterative filtering operation steps are repeated until the glaucoma distinguishing detection data quality accuracy assessment index is greater than or equal to the glaucoma distinguishing detection data quality accuracy assessment index comprehensive threshold.

2. The method for detecting glaucoma distinction based on multi-mode fusion according to claim 1, wherein the specific steps of collecting and processing glaucoma distinction detection data are as follows:

collecting glaucoma distinguishing and detecting original data through a sensor device;

and cleaning and denoising the glaucoma distinguishing detection raw data to obtain glaucoma distinguishing detection data, wherein the glaucoma distinguishing detection data comprises glaucoma distinguishing detection data integrity data and glaucoma distinguishing detection data image quality data.

3. The method for distinguishing glaucoma based on multi-mode fusion according to claim 1, wherein the specific steps for obtaining the integrity evaluation index of the distinguishing glaucoma detection data are as follows:

Obtaining glaucoma distinguishing detection data entropy values of a preset glaucoma distinguishing detection data integrity detection point through image processing software;

Obtaining a run length of a preset glaucoma distinguishing detection data integrity detection point through image compression software;

Obtaining a peak signal intensity maximum value of a preset glaucoma distinguishing detection data integrity detection point through image processing software;

Obtaining a minimum peak signal intensity value of a preset glaucoma distinguishing detection data integrity detection point through image processing software;

obtaining image noise of a preset glaucoma distinguishing detection data integrity detection point through a noise analysis tool;

obtaining the image artifact intensity of a preset glaucoma distinguishing detection data integrity detection point through image processing software;

Obtaining an ambient illumination angle of a preset glaucoma distinguishing detection data integrity detection point through illumination measurement equipment;

the glaucoma distinguishing detection data integrity data comprises glaucoma distinguishing detection data entropy value, run length, peak signal intensity maximum value, peak signal intensity minimum value, image noise, image artifact intensity and ambient illumination angle;

And analyzing the integrity data of the glaucoma distinguishing detection data to obtain an integrity evaluation index of the glaucoma distinguishing detection data.

4. The method for detecting glaucoma distinction based on multi-mode fusion according to claim 1, wherein the specific steps for obtaining the image quality evaluation index of the glaucoma distinction detection data are as follows:

Obtaining an image compression ratio of an image quality detection point of preset glaucoma distinguishing detection data through image compression software;

obtaining the run length of a preset glaucoma distinguishing detection data image quality detection point through image compression software;

obtaining the image contrast of an image quality detection point of preset glaucoma distinguishing detection data through image processing software;

Obtaining the image brightness of an image quality detection point of preset glaucoma distinguishing detection data through image processing software;

Obtaining image noise of an image quality detection point of preset glaucoma distinguishing detection data through image processing software;

obtaining the image artifact intensity of an image quality detection point of preset glaucoma distinguishing detection data through image processing software;

The glaucoma distinguishing detection data comprises image quality data including image compression ratio, run length, image contrast, image brightness, image noise and image artifact intensity;

And analyzing the image quality data of the glaucoma distinguishing detection data to obtain an image quality evaluation index of the glaucoma distinguishing detection data.

5. The method for distinguishing and detecting glaucoma based on multi-mode fusion according to claim 1, wherein the specific steps for comprehensively analyzing and obtaining the quality accuracy evaluation index of the distinguishing and detecting glaucoma data are as follows:

obtaining the image resolution of a preset glaucoma distinguishing detection data quality accuracy detection point through image processing software;

And obtaining the glaucoma distinguishing detection data quality accuracy assessment index through comprehensive analysis of the glaucoma distinguishing detection data integrity assessment index, the glaucoma distinguishing detection data image quality assessment index and the image resolution.

6. The method for detecting glaucoma distinction based on multi-mode fusion according to claim 1, wherein the specific steps of the method for optimizing and adjusting the accuracy of the quality of glaucoma distinction detection data are as follows:

Extracting a glaucoma distinguishing detection data quality accuracy evaluation index comprehensive threshold value from a glaucoma distinguishing detection database, and comparing the glaucoma distinguishing detection data quality accuracy evaluation index with the glaucoma distinguishing detection data quality accuracy evaluation index comprehensive threshold value;

If the glaucoma distinguishing detection data quality accuracy evaluation index is greater than or equal to the glaucoma distinguishing detection data quality accuracy evaluation index comprehensive threshold, the glaucoma distinguishing detection data quality accuracy does not need to be optimized and adjusted.

7. A system for applying the multi-modal fusion-based glaucoma distinguishing detection method according to any one of claims 1-6, comprising a module for collecting glaucoma distinguishing detection data, a module for analyzing glaucoma distinguishing detection data, a comprehensive analysis module, and a module for optimizing and adjusting accuracy of quality of glaucoma distinguishing detection data:

The glaucoma distinguishing detection data acquisition module is used for acquiring and processing glaucoma distinguishing detection data;

the glaucoma distinguishing detection data analysis module is used for analyzing the glaucoma distinguishing detection data to obtain an integrity evaluation index of the glaucoma distinguishing detection data and an image quality evaluation index of the glaucoma distinguishing detection data;

the comprehensive analysis module is used for comprehensively analyzing to obtain glaucoma distinguishing detection data quality accuracy assessment index;

The glaucoma distinguishing detection data quality accuracy optimization adjustment method comprises the steps of comparing and analyzing the glaucoma distinguishing detection data integrity evaluation index with a first threshold value of the glaucoma distinguishing detection data integrity evaluation index, optimizing and adjusting the glaucoma distinguishing detection data integrity method, comparing and analyzing the glaucoma distinguishing detection data image quality evaluation index with a second threshold value of the glaucoma distinguishing detection data image quality evaluation index, optimizing and adjusting the glaucoma distinguishing detection data image quality method, comparing and analyzing the glaucoma distinguishing detection data quality accuracy evaluation index with a comprehensive threshold value of the glaucoma distinguishing detection data quality accuracy evaluation index, and optimizing and adjusting the glaucoma distinguishing detection data quality accuracy method.