CN110610498A - Mammary gland molybdenum target image processing method, system, storage medium and equipment - Google Patents
Mammary gland molybdenum target image processing method, system, storage medium and equipment Download PDFInfo
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Abstract
The application relates to a mammary gland molybdenum target image processing method, a system, a storage medium and equipment, which are used for acquiring a mammary gland molybdenum target image; carrying out image segmentation processing on the mammary gland molybdenum target image to obtain a target structure; after the target structure is obtained, performing image enhancement processing on the target structure in the mammary gland molybdenum target image to obtain a mammary gland molybdenum target enhanced image containing the enhanced target structure; or after the target structure is obtained, obtaining an original signal shot by the molybdenum target, performing signal enhancement processing on a target signal corresponding to the target structure in the original signal, and obtaining a corresponding breast molybdenum target enhanced image according to the enhanced target signal and the original signal. After the mammary gland molybdenum target image is obtained, the target structure in the mammary gland molybdenum target image is segmented and enhanced, so that the target structure in the mammary gland molybdenum target image can be distinguished more easily, a doctor can observe and analyze the mammary gland molybdenum target image, and the diagnosis accuracy is improved.
Description
Technical Field
The application relates to the technical field of medical images, in particular to a mammary gland molybdenum target image processing method, a system, a storage medium and equipment.
Background
The mammary gland molybdenum target examination (mammogram) is the simplest and most reliable noninvasive detection means for diagnosing mammary gland diseases at present, has the characteristics of simplicity, convenience, high resolution, good repeatability, capability of keeping images for comparison before and after, no limitation by age and body form and the like, and is one of the accepted best methods for clinical routine examination of breast cancer lesions and breast cancer prevention screening.
During breast molybdenum target examination, breast density is critical to the diagnosis of breast mammogram. Clinically, gland density is generally graded according to how much the breast gland is represented in the image and the international diagnostic standard BIRADS. However, since the breast gland tissue appears as a highlight region in the mammogram image, if the position of the lesion is in the vicinity of or inside the breast gland tissue, when the breast density is large, a missed diagnosis situation is easily caused. Therefore, the prior art has the problem of low diagnosis accuracy.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a breast molybdenum target image processing method, system, storage medium, and device that can improve the diagnostic accuracy.
A breast molybdenum target image processing method comprises the following steps:
acquiring a mammary gland molybdenum target image;
carrying out image segmentation processing on the mammary gland molybdenum target image to obtain a target structure;
after the target structure is obtained, performing image enhancement processing on the target structure in the mammary gland molybdenum target image to obtain a mammary gland molybdenum target enhanced image containing the enhanced target structure;
or after the target structure is obtained, obtaining an original signal shot by the molybdenum target, performing signal enhancement processing on a target signal corresponding to the target structure in the original signal, and obtaining a corresponding breast molybdenum target enhanced image according to the enhanced target signal and the original signal.
A breast molybdenum target image processing system, comprising:
the shooting device is used for shooting the molybdenum target of the mammary gland to obtain an original signal shot by the molybdenum target;
the image segmentation module is used for carrying out image segmentation processing on the mammary gland molybdenum target image corresponding to the original signal to obtain a target structure;
the image enhancement module is used for carrying out image enhancement processing on the target structure in the mammary gland molybdenum target image after the target structure is obtained by the image segmentation module so as to obtain a mammary gland molybdenum target enhancement image containing the enhanced target structure; or after the image segmentation module obtains the target structure, obtaining the original signal shot by the molybdenum target, performing signal enhancement processing on a target signal corresponding to the target structure in the original signal, and obtaining a corresponding mammary molybdenum target enhanced image according to the enhanced target signal and the original signal.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the above method when executing the computer program.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method.
The mammary gland molybdenum target image processing method, the mammary gland molybdenum target image processing device, the storage medium and the equipment are used for acquiring a mammary gland molybdenum target image; carrying out image segmentation processing on the mammary gland molybdenum target image to obtain a target structure; after the target structure is obtained, performing image enhancement processing on the target structure in the mammary gland molybdenum target image to obtain a mammary gland molybdenum target enhanced image containing the enhanced target structure; or after the target structure is obtained, obtaining an original signal shot by the molybdenum target, performing signal enhancement processing on a target signal corresponding to the target structure in the original signal, and obtaining a corresponding breast molybdenum target enhanced image according to the enhanced target signal and the original signal. After the mammary gland molybdenum target image is obtained, the target structure in the mammary gland molybdenum target image is segmented and enhanced, so that the target structure in the mammary gland molybdenum target image can be distinguished more easily, a doctor can observe and analyze the mammary gland molybdenum target image, and the diagnosis accuracy is improved.
Drawings
FIG. 1 is a schematic flow chart of a breast molybdenum target image processing method according to an embodiment;
FIG. 2 is a diagram illustrating an example of an image segmentation process performed on a breast molybdenum target image according to an embodiment;
FIG. 3 is a diagram illustrating another example of image segmentation processing performed on a breast molybdenum target image according to one embodiment;
fig. 4 is a schematic flow chart illustrating a process of performing signal enhancement processing on a target signal corresponding to a target structure in an original signal and obtaining a corresponding breast molybdenum target enhanced image according to the enhanced target signal and the original signal in one embodiment;
FIG. 5 is a schematic diagram of a breast molybdenum target image processing system according to an embodiment;
FIG. 6 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In one embodiment, as shown in fig. 1, a breast molybdenum target image processing method is provided, which is explained by taking an example that the method is applied to a processor capable of performing breast molybdenum target image processing, and the method includes the following steps:
and S100, acquiring a mammary gland molybdenum target image.
Specifically, the processor can reconstruct and correct the image of the data of the object to be detected acquired by the mammary gland molybdenum target inspection system, so as to obtain the mammary gland molybdenum target image corresponding to the object to be detected. Of course, the breast molybdenum target image can be reconstructed and corrected in advance and stored in the memory of the computer device, and when the breast molybdenum target image needs to be processed, the processor directly reads the breast molybdenum target image from the memory of the computer device. Of course, the processor may also acquire an image of the breast molybdenum target from an external device. For example, the mammary gland molybdenum target image of the object to be detected is stored in the cloud, and when the processing operation is required, the processor acquires the mammary gland molybdenum target image of the object to be detected from the cloud. The embodiment does not limit the specific way for the processor to acquire the breast molybdenum target image.
And S200, carrying out image segmentation processing on the mammary gland molybdenum target image to obtain a target structure.
Image segmentation processing refers to techniques and processes that divide an image into a number of specific regions with unique properties and pose objects of interest. In this embodiment, after obtaining the breast molybdenum target image, the processor performs image segmentation processing on the breast molybdenum target image to obtain a specific target structure serving as an interested target.
Optionally, the target structure comprises: at least one of breast integrity, breast gland and lesion. The whole breast is the contour of the breast. The Breast density of a Breast gland is an important index in Breast examination, the Breast density clinically refers to how much the Breast gland is represented in an image, and the international diagnostic standard BIRADS (Breast Imaging Reporting and Data System, American society for radiology Breast image report and Data System) divides the gland density into four grades of ABCD. Wherein, A is fat gland (gland tissue ratio is lower than 25%), B is small gland (gland tissue ratio is 25% -50%), C is large gland (gland tissue ratio is 50% -75%), D is dense gland (gland tissue ratio is more than 75%). The lesion refers to a portion of the breast where a lesion occurs, such as a lump, a calcified cluster, a nodule, or the like.
Specifically, as shown in fig. 2 and 3, an example of image segmentation processing on a breast molybdenum target image is shown, fig. 2(a) and 3(a) are breast molybdenum target images, fig. 2(b) and 3(b) are segmented images of the whole breast, and fig. 2(c) and 3(c) are segmented images of a breast gland. In addition, since the types of the lesions are many, the example image is not shown for the segmentation image of the lesions.
When the image of the breast molybdenum target is divided, the specific image division method used by the processor is not limited, and for example, the image division process may be performed by any one of a threshold-based division method, a region-based division method, an edge-based division method, a specific theory-based division method, and the like, or may be performed by another method using a network model.
Among them, the threshold-based segmentation method is one of the most common, most direct, and most basic image segmentation methods for detecting a target region. The threshold segmentation method considers that similar gray values exist between adjacent pixels in the object or the background, and a certain difference exists in gray level between pixels of different objects or backgrounds. Therefore, the threshold segmentation method divides the image into different regions according to a certain threshold, and there are usually two kinds of thresholds, namely a global threshold and a local threshold. The image segmentation method based on the region is based on the gray scale of the image region, the texture change and a plurality of statistical characteristics such as variance, probability density and the like, and the region of interest of the image lesion is extracted. A lesion such as the inside of a lump region has the same certain regional characteristic attribute and can be distinguished from the external characteristic of the lump region, so that the regions with the same characteristic are combined together, the regions with different characteristics are separated, and the purpose of segmenting the lesion region can be achieved. The region-based segmentation method includes a region growing and splitting combination method and the like. The image edge refers to a set of pixels with larger difference in gray level of image pixels, exists between a target and a target, and exists between the target and a background, and is an important characteristic considered by image segmentation. The brightness values of the inner and outer regions of the boundary are discontinuous, and the local change characteristics of the image can be reflected. Edge-based segmentation techniques segment images by detecting whether pixel points within the image are edge points. Typical edge detection methods include a serial edge detection method, a parallel edge detection method, hough transform, curve fitting, and the like. In most medical image segmentation processes, since the image quality and the surrounding tissues are easily affected, it is relatively difficult to obtain a good segmentation result simply by relying on relevant conditions such as a threshold value, gray scale information or edge information. Therefore, medical image segmentation methods based on specific theories, such as wavelet transform-based image segmentation techniques, level set-based image segmentation techniques, blur theory-based image segmentation techniques, knowledge-based image segmentation techniques, and the like, may be employed.
Step S310, after the target structure is obtained, image enhancement processing is carried out on the target structure in the mammary gland molybdenum target image, and the mammary gland molybdenum target enhanced image containing the enhanced target structure is obtained.
The image enhancement processing is to purposefully emphasize the overall or local characteristics of an image, change an original unclear image into clear or emphasize some interesting features, enlarge the difference between different object features in the image, inhibit the uninteresting features, improve the image quality and the information content, enhance the image interpretation and identification effects, and meet the requirements of some special analyses. In the radiation imaging process of digital X-ray medical images, the quality of the images is reduced due to the complexity of human structures and tissues and the influence of various adverse factors such as X-ray scattering and electronic noise in the imaging system, which mainly shows that the images have poor contrast and blurred edge details, and are not beneficial to diagnosis by doctors. In addition, in the breast molybdenum target image, since the breast gland is represented as a highlight area and may be confused with a lesion, in order to increase the contrast ratio for distinguishing, the processor continues to perform the image enhancement processing on the target structure after obtaining the target structure through the image segmentation processing, so as to obtain the breast molybdenum target enhanced image including the enhanced target structure, thereby improving the visual effect of the breast molybdenum target enhanced image.
Alternatively, the process of obtaining the breast molybdenum target enhanced image by the processor can be realized by the following steps:
and step S320, after the target structure is obtained, obtaining an original signal shot by the molybdenum target, performing signal enhancement processing on a target signal corresponding to the target structure in the original signal, and obtaining a corresponding mammary molybdenum target enhanced image according to the enhanced target signal and the original signal.
The original signals are mammary X-ray bare signals obtained through mammary molybdenum target shooting, the processor screens corresponding target signals from the original signals according to the target structures after obtaining the target structures through image segmentation processing, then performs signal enhancement processing on the target signals to improve the signal effect of the target signals, and finally obtains corresponding mammary molybdenum target enhanced images according to the enhanced target signals and the original signals.
Generally, the visual processing process mainly comprises: the optical signal enters the lens, the sensor senses the optical signal, the sensor converts the sensed optical signal into an electrical signal and sends the electrical signal to the imaging engine, the imaging engine images according to the electrical signal and sends the electrical signal to a Graphics Processing Unit (GPU), and the GPU outputs an image result. The signal enhancement processing may specifically be to enhance an optical signal sensed by the sensor in the above process, or to enhance an electrical signal converted by the sensor, and is applied to the scenario of the present application, where the signal enhancement processing may be to enhance an X-ray signal detected by a detector (detector), or to enhance an electrical signal converted by the detector. In addition, when the signal is enhanced, the enhancement may be implemented by using an enhancement method in the prior art, and is not limited herein.
In this embodiment, the breast molybdenum target enhanced image can be obtained in step S310 or step S320, and when a doctor performs observation and diagnosis through the breast molybdenum target enhanced image, because the target structure in the breast molybdenum target enhanced image is the target structure after image enhancement processing, the display capability of an unobvious structure is improved, so that the doctor can perform observation and diagnosis more clearly, thereby improving the diagnosis accuracy.
After the mammary gland molybdenum target image is obtained, the target structure in the image is segmented and the target image is subjected to enhancement processing, so that the target structure in the mammary gland molybdenum target image can be distinguished more easily, a doctor can observe and analyze the mammary gland molybdenum target image, and the diagnosis accuracy is improved.
In one embodiment, the image segmentation processing is carried out on the mammary gland molybdenum target image to obtain the target structure, and the image segmentation processing is carried out on the mammary gland molybdenum target image through a deep learning image segmentation model to obtain the target structure.
Specifically, sample data can be used for performing deep learning training on the network model in advance to obtain a trained deep learning image segmentation model, wherein the sample data comprises a breast molybdenum target sample image, and the breast molybdenum target sample image comprises the label of each target structure. The training process of the network model may be implemented by an existing training method, and is not specifically limited herein. After the trained deep learning image segmentation model is obtained, the processor performs image segmentation processing on the mammary gland molybdenum target image by using the deep learning image segmentation model, so that a target structure needing enhancement processing is obtained.
The embodiment performs the image segmentation processing by using the deep learning image segmentation model, so that the accuracy of the image segmentation processing result can be improved as the deep learning effect on the image processing is better.
In one embodiment, the image enhancement processing is performed on the target structure in the breast molybdenum target image, and comprises the following steps: and carrying out image detail enhancement processing on the target structure in the mammary gland molybdenum target image.
Specifically, in a breast molybdenum target image, the target structure may have a situation of insufficient local detail, and in order to further improve the image local detail effect, image multi-scale local detail promotion processing may be performed. For example, the breast target image may be subjected to multi-scale gaussian blurring, image details of different degrees are obtained by subtraction, and then detail information of different degrees is fused into the original image through a suitable weight coefficient, wherein the detail information of different degrees is obtained by subtracting the blurred image from the original image, the detail images of different degrees are added with a certain weight to obtain rich and complete detail components, and the detail components are superimposed on the basis of the original image, so as to achieve the purpose of improving the overall visual effect of the image.
According to the embodiment, the image detail enhancement processing is performed on the target structure in the breast molybdenum target image, so that the local detail of the target structure can be improved, and the accuracy and the reliability of a diagnosis result can be improved.
In one embodiment, the image enhancement processing is performed on the target structure in the breast molybdenum target image, and comprises the following steps: and carrying out image contrast enhancement processing on the target structure in the breast molybdenum target image.
Image contrast refers to the degree of contrast between light and dark in an image. The contrast generally represents the degree of sharpness of the image quality. The larger the contrast is, the more gradation from black to white is, the richer the expression ability of the gradation is, and the clearer and more conspicuous the image is. The smaller the contrast, the lower the sharpness of the picture, and the poorer the gradation. Therefore, by enlarging the difference in brightness (i.e., contrast), a portion of the image that is of interest can be emphasized.
According to the method, the target structure in the breast molybdenum target image is subjected to image contrast enhancement processing, so that the target structure and other non-target structures can be better distinguished, and the readability of the image is improved, thereby being beneficial to improving the accuracy and reliability of a diagnosis result.
In one embodiment, the image enhancement processing is performed by at least one of gray scale transformation, histogram modification, edge enhancement, high frequency emphasis filtering, and deconvolution.
The gray level transformation is a method for changing the gray level value of each pixel in a source image point by point according to a certain transformation relation according to a certain target condition, and aims to improve the image quality and enable the display effect of the image to be clearer. . Due to the limitation of an imaging system, the defect of insufficient contrast often occurs, so that the visual effect of human eyes is poor when the human eyes watch images. The gray level transformation mainly processes independent pixel points, and the image is visually well changed by changing the gray level range occupied by the original image data. Even the same image will yield different results if the selected gray scale transformation functions are different. Therefore, the selection of the gradation conversion function should be decided according to the nature of the image and the purpose of the processing. The selection standard is that after gray level conversion, the dynamic range of pixels is increased, the contrast of the image is expanded, and the image becomes clearer, finer and smoother and is easy to identify.
The histogram modification is divided into histogram equalization and histogram specification (histogram matching), wherein the histogram equalization is a method for modifying the gray histogram of the original image into a uniform histogram by performing some transformation on the original image, and the contrast of the dark image and the low-contrast image can be effectively enhanced by using the image histogram for the histogram equalization; the histogram specification is an enhancement method for modifying an image by changing the original image gradation histogram into a histogram of a predetermined shape. After the histogram is trimmed, the gray scale interval of the image can be separated or the gray scale distribution is uniform, so that the contrast is increased, the image details are clear, and the image is enhanced.
In the frequency domain space, the information of the image appears as a combination of different frequency components. Smooth regions in the image correspond to low frequency components and edges and details in the image correspond to high frequency components, so that sharpening the image can be achieved by suppressing low frequency components in the fourier transform of the specified image, and by suppressing high frequency components. Common high-pass filters are: an ideal high-pass filter, a butterworth high-pass filter, a gaussian high-pass filter. The high-pass filter attenuates the low frequency components of the fourier transform while leaving the high frequency components relatively unchanged, which highlights the edges and details of the image, making the image edges clearer. But since the high pass filter deviates from the dc component, the average grey value of the image is reduced to 0. One way of compensating is to add an offset to the high pass filter, which is called high frequency emphasis filtering if the offset is combined with the filter multiplied by a constant greater than l. The high-frequency emphasis filtering enhances the high-frequency information of the image and simultaneously retains the low-frequency information of the image, has small influence on the whole gray scale of the image, and can effectively enhance the edge details of the image.
Deconvolution is a computationally intensive image processing technique that relies primarily on a series of deblurring techniques to improve image quality, with light and object interactions arising primarily from physical phenomena such as scattering, glare and blurring. However, the composition and arrangement of molecules in a particular material (whether glass, water or protein) has particular optical properties. The purpose of deconvolution is to distinguish the locations where scattering, glare and blurring occur and the possibility of creating mathematical models of these phenomena. Because scattering occurs in a sample in an unstable position, it is considered difficult to model. In contrast, because blur is a function of the microscope optical system, it is easy to model. Such a model makes deblurring possible, and deconvolution can use this model to deblur. By deconvoluting the image, the contrast and sharpness of the image can be improved.
In one embodiment, for a mammary gland molybdenum target image with dark brightness or poor imaging effect, after a target structure is obtained through image segmentation, high-frequency emphasis filtering can be used for enhancing details of the target structure, and then histogram equalization is used for enhancing the contrast of the target structure, so that the image effect of the target structure in the mammary gland molybdenum target image is improved.
In one embodiment, when the types of target structures are two or more, the image enhancement effect is different for different types of target structures.
In particular, during the breast examination based on the molybdenum target image of the breast, the display effect desired by the doctor is different for different target structures, for example, the doctor may desire higher contrast for the whole breast and better detail effect for the breast gland. Thus, when the types of target structures are two or more, the processor may employ different image enhancement methods for different types of target structures, for example, histogram equalization may be employed to enhance contrast of the breast as a whole, followed by high frequency emphasis filtering to enhance details of the breast gland. Therefore, the image effects of different target structures are different, so that the doctor can observe and diagnose more conveniently.
In one embodiment, as shown in fig. 4, a target signal corresponding to a target structure in an original signal is subjected to signal enhancement processing, and a corresponding breast molybdenum target enhanced image is obtained according to the enhanced target signal and the original signal, including steps S322 to S324.
Step S322, performing signal enhancement processing on a target signal corresponding to a target structure in the original signal to obtain an enhanced target signal;
and S324, carrying out image reconstruction processing according to the enhanced target signal and the original signal to obtain a corresponding breast molybdenum target enhanced image.
Specifically, after the processor obtains a target structure through image segmentation processing, corresponding target signals are screened from original signals according to the target structure, then signal enhancement processing is performed on the target signals to improve the signal effect of the target signals, and finally image reconstruction processing is performed according to the enhanced target signals and the original signals to obtain corresponding mammary molybdenum target enhanced images.
In the embodiment, the mode of signal enhancement on the target signal corresponding to the target structure in the original signal is adopted, so that the image display effect of the target structure in the reconstructed image can be improved, and the accuracy of the diagnosis result observed by a doctor is improved.
It should be understood that, under reasonable circumstances, although the steps in the flowcharts referred to in the foregoing embodiments are shown in sequence as indicated by the arrows, the steps are not necessarily executed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in each flowchart may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of performing the sub-steps or stages is not necessarily sequential, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 5, a breast molybdenum target image processing system is provided, which includes a camera 100, an image segmentation module 200, and an image enhancement module 300.
The shooting device 100 is used for shooting the molybdenum target of the mammary gland to obtain an original signal shot by the molybdenum target;
the image segmentation module 200 is configured to perform image segmentation processing on a breast molybdenum target image corresponding to the original signal to obtain a target structure;
the image enhancement module 300 is configured to perform image enhancement processing on the target structure in the breast molybdenum target image after the image segmentation module obtains the target structure, so as to obtain a breast molybdenum target enhanced image containing the enhanced target structure; or after the image segmentation module obtains the target structure, obtaining an original signal shot by the molybdenum target, performing signal enhancement processing on a target signal corresponding to the target structure in the original signal, and obtaining a corresponding breast molybdenum target enhanced image according to the enhanced target signal and the original signal.
In one embodiment, the target structure comprises: at least one of breast integrity, breast gland and lesion.
In one embodiment, the image segmentation module 200 is to: and carrying out image segmentation processing on the mammary gland molybdenum target image through a deep learning image segmentation model to obtain the target structure.
In one embodiment, the image enhancement module 300 is configured to: and performing image detail enhancement processing on the target structure in the breast molybdenum target image.
In one embodiment, the image enhancement module 300 is configured to: and carrying out image contrast enhancement treatment on the target structure in the breast molybdenum target image.
In one embodiment, the image enhancement processing is implemented by at least one of gray scale transformation, histogram modification, edge enhancement, high frequency emphasis filtering, and deconvolution.
In one embodiment, when the types of the target structures are two or more, the image enhancement effects of different types of target structures are different.
In one embodiment, the image enhancement module 300 is configured to: performing signal enhancement processing on a target signal corresponding to the target structure in the original signal to obtain an enhanced target signal; and carrying out image reconstruction processing according to the enhanced target signal and the original signal to obtain a corresponding breast molybdenum target enhanced image.
For specific limitations of the breast molybdenum target image processing system, reference may be made to the above limitations of the breast molybdenum target image processing method, which are not described herein again. All or part of the modules in the breast molybdenum target image processing system can be implemented by software, hardware and a combination thereof, for example, the photographing device 100 can be implemented by hardware, and the image segmentation module 200 and the image enhancement module 300 can be implemented by software. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program: acquiring a mammary gland molybdenum target image; carrying out image segmentation processing on the mammary gland molybdenum target image to obtain a target structure; after the target structure is obtained, carrying out image enhancement processing on the target structure in the mammary gland molybdenum target image to obtain a mammary gland molybdenum target enhanced image containing the enhanced target structure; or after the target structure is obtained, obtaining an original signal shot by the molybdenum target, performing signal enhancement processing on a target signal corresponding to the target structure in the original signal, and obtaining a corresponding breast molybdenum target enhanced image according to the enhanced target signal and the original signal.
In one embodiment, the processor, when executing the computer program, further performs the steps of: and carrying out image segmentation processing on the mammary gland molybdenum target image through a deep learning image segmentation model to obtain a target structure.
In one embodiment, the processor, when executing the computer program, further performs the steps of: and carrying out image detail enhancement processing on the target structure in the mammary gland molybdenum target image.
In one embodiment, the processor, when executing the computer program, further performs the steps of: and carrying out image contrast enhancement processing on the target structure in the breast molybdenum target image.
In one embodiment, the processor, when executing the computer program, further performs the steps of: performing signal enhancement processing on a target signal corresponding to a target structure in an original signal to obtain an enhanced target signal; and carrying out image reconstruction processing according to the enhanced target signal and the original signal to obtain a corresponding breast molybdenum target enhanced image.
FIG. 6 is a diagram illustrating an internal structure of a computer device in one embodiment. The computer device may specifically be a terminal (or server). As shown in fig. 6, the computer apparatus includes a processor, a memory, a network interface, an input device, and a display screen connected through a system bus. Wherein the memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program that, when executed by the processor, causes the processor to implement a video bitrate control method and a video transcoding method. The internal memory may also store a computer program, which when executed by the processor, causes the processor to perform a video bitrate control method and a video transcoding method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 6 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of: acquiring a mammary gland molybdenum target image; carrying out image segmentation processing on the mammary gland molybdenum target image to obtain a target structure; after the target structure is obtained, carrying out image enhancement processing on the target structure in the mammary gland molybdenum target image to obtain a mammary gland molybdenum target enhanced image containing the enhanced target structure; or after the target structure is obtained, obtaining an original signal shot by the molybdenum target, performing signal enhancement processing on a target signal corresponding to the target structure in the original signal, and obtaining a corresponding breast molybdenum target enhanced image according to the enhanced target signal and the original signal.
In one embodiment, the computer program when executed by the processor further performs the steps of: and carrying out image segmentation processing on the mammary gland molybdenum target image through a deep learning image segmentation model to obtain a target structure.
In one embodiment, the computer program when executed by the processor further performs the steps of: and carrying out image detail enhancement processing on the target structure in the mammary gland molybdenum target image.
In one embodiment, the computer program when executed by the processor further performs the steps of: and carrying out image contrast enhancement processing on the target structure in the breast molybdenum target image.
In one embodiment, the computer program when executed by the processor further performs the steps of: performing signal enhancement processing on a target signal corresponding to a target structure in an original signal to obtain an enhanced target signal; and carrying out image reconstruction processing according to the enhanced target signal and the original signal to obtain a corresponding breast molybdenum target enhanced image.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware related to instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, the computer program can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A breast molybdenum target image processing method is characterized by comprising the following steps:
acquiring a mammary gland molybdenum target image;
carrying out image segmentation processing on the mammary gland molybdenum target image to obtain a target structure;
after the target structure is obtained, performing image enhancement processing on the target structure in the mammary gland molybdenum target image to obtain a mammary gland molybdenum target enhanced image containing the enhanced target structure;
or after the target structure is obtained, obtaining an original signal shot by the molybdenum target, performing signal enhancement processing on a target signal corresponding to the target structure in the original signal, and obtaining a corresponding breast molybdenum target enhanced image according to the enhanced target signal and the original signal.
2. The method of claim 1, wherein the target structure comprises: at least one of breast integrity, breast gland and lesion.
3. The method of claim 1, wherein performing image segmentation on the breast molybdenum target image to obtain a target structure comprises:
and carrying out image segmentation processing on the mammary gland molybdenum target image through a deep learning image segmentation model to obtain the target structure.
4. The method of claim 1, wherein image enhancing the target structure in the breast molybdenum target image comprises at least one of:
the first item: performing image detail enhancement processing on the target structure in the breast molybdenum target image;
the second term is: and carrying out image contrast enhancement treatment on the target structure in the breast molybdenum target image.
5. The method according to claim 1 or 4, wherein the image enhancement processing is implemented by at least one of gray scale transformation, histogram modification, edge enhancement, high frequency emphasis filtering, and deconvolution.
6. The method of claim 2, wherein when the types of the target structures are two or more, the image enhancement effect is different for different types of target structures.
7. The method of claim 1, wherein performing signal enhancement processing on a target signal corresponding to the target structure in the original signal, and obtaining a corresponding breast molybdenum target enhanced image according to the enhanced target signal and the original signal comprises:
performing signal enhancement processing on a target signal corresponding to the target structure in the original signal to obtain an enhanced target signal;
and carrying out image reconstruction processing according to the enhanced target signal and the original signal to obtain a corresponding breast molybdenum target enhanced image.
8. A breast molybdenum target image processing system, comprising:
the shooting device is used for shooting the molybdenum target of the mammary gland to obtain an original signal shot by the molybdenum target;
the image segmentation module is used for carrying out image segmentation processing on the mammary gland molybdenum target image corresponding to the original signal to obtain a target structure;
the image enhancement module is used for carrying out image enhancement processing on the target structure in the mammary gland molybdenum target image after the target structure is obtained by the image segmentation module so as to obtain a mammary gland molybdenum target enhancement image containing the enhanced target structure; or after the image segmentation module obtains the target structure, obtaining the original signal shot by the molybdenum target, performing signal enhancement processing on a target signal corresponding to the target structure in the original signal, and obtaining a corresponding mammary molybdenum target enhanced image according to the enhanced target signal and the original signal.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of claims 1 to 7.
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