CN116369824A - Endoscope image processing system and algorithm - Google Patents
Endoscope image processing system and algorithm Download PDFInfo
- Publication number
- CN116369824A CN116369824A CN202310380138.8A CN202310380138A CN116369824A CN 116369824 A CN116369824 A CN 116369824A CN 202310380138 A CN202310380138 A CN 202310380138A CN 116369824 A CN116369824 A CN 116369824A
- Authority
- CN
- China
- Prior art keywords
- image
- endoscope
- unit
- module
- focus
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000006243 chemical reaction Methods 0.000 claims abstract 8
- 238000003707 image sharpening Methods 0.000 claims abstract 5
- 238000000605 extraction Methods 0.000 claims abstract 3
- 238000003702 image correction Methods 0.000 claims abstract 3
- 230000002457 bidirectional effect Effects 0.000 claims 4
- 238000001727 in vivo Methods 0.000 claims 4
- 201000010099 disease Diseases 0.000 claims 2
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims 2
- 238000000338 in vitro Methods 0.000 claims 2
- 230000003902 lesion Effects 0.000 claims 2
- 238000003705 background correction Methods 0.000 claims 1
- 238000004364 calculation method Methods 0.000 claims 1
- 239000003086 colorant Substances 0.000 claims 1
- 238000001514 detection method Methods 0.000 claims 1
- 238000012544 monitoring process Methods 0.000 claims 1
- 230000009286 beneficial effect Effects 0.000 abstract 1
Images
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B1/00—Instruments for performing medical examinations of the interior of cavities or tubes of the body by visual or photographical inspection, e.g. endoscopes; Illuminating arrangements therefor
- A61B1/00002—Operational features of endoscopes
- A61B1/00004—Operational features of endoscopes characterised by electronic signal processing
- A61B1/00009—Operational features of endoscopes characterised by electronic signal processing of image signals during a use of endoscope
- A61B1/000094—Operational features of endoscopes characterised by electronic signal processing of image signals during a use of endoscope extracting biological structures
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B1/00—Instruments for performing medical examinations of the interior of cavities or tubes of the body by visual or photographical inspection, e.g. endoscopes; Illuminating arrangements therefor
- A61B1/00002—Operational features of endoscopes
- A61B1/00004—Operational features of endoscopes characterised by electronic signal processing
- A61B1/00009—Operational features of endoscopes characterised by electronic signal processing of image signals during a use of endoscope
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B1/00—Instruments for performing medical examinations of the interior of cavities or tubes of the body by visual or photographical inspection, e.g. endoscopes; Illuminating arrangements therefor
- A61B1/04—Instruments for performing medical examinations of the interior of cavities or tubes of the body by visual or photographical inspection, e.g. endoscopes; Illuminating arrangements therefor combined with photographic or television appliances
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/50—Constructional details
- H04N23/555—Constructional details for picking-up images in sites, inaccessible due to their dimensions or hazardous conditions, e.g. endoscopes or borescopes
Landscapes
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Surgery (AREA)
- Radiology & Medical Imaging (AREA)
- General Health & Medical Sciences (AREA)
- Physics & Mathematics (AREA)
- Medical Informatics (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Heart & Thoracic Surgery (AREA)
- Veterinary Medicine (AREA)
- Biomedical Technology (AREA)
- Optics & Photonics (AREA)
- Biophysics (AREA)
- Molecular Biology (AREA)
- Animal Behavior & Ethology (AREA)
- Signal Processing (AREA)
- Public Health (AREA)
- Pathology (AREA)
- Multimedia (AREA)
- Quality & Reliability (AREA)
- Computer Vision & Pattern Recognition (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Endoscopes (AREA)
- Closed-Circuit Television Systems (AREA)
Abstract
The invention discloses an endoscope image processing system and an algorithm, comprising an endoscope image receiving unit, an image correction unit, a color conversion processing unit, an image noise reduction unit, an image sharpening unit, an endoscope external image removing unit, an endoscope position identification unit, a focus image model library and a focus group extraction unit, wherein the beneficial effects of the invention are that: by adding the endoscope position identification unit, the endoscopic image and the focus image are checked, contrast is carried out, and the focus image existing in the endoscopic image is identified, so that the focus judgment processing is carried out on the endoscopic image.
Description
Technical Field
The invention relates to the technical field of endoscopes, in particular to an endoscope image processing system and an algorithm.
Background
The endoscope operation belongs to a minimally invasive diagnosis and treatment technology, and can be used for checking infectious or pathological diseases of tissue systems such as abdominal cavity, intestines and stomach, bladder, urinary tract and the like by means of a visual endoscope instrument entering the body from an operation incision or from a natural duct, and the internal form and the internal environment can be observed through the illumination transmission and image capturing functions of the endoscope.
The endoscope acquires images through an image capturing function, transmits the images to an external image processing device for image processing, and finally outputs image signals to a visualization instrument for display and is used for observation.
In the existing endoscope image, the existing in-vitro image is inconvenient to extract for the image processing, the observation efficiency for the endoscope image processing is low, and the image observation is inconvenient.
Disclosure of Invention
The present invention is directed to an endoscopic image processing system and an algorithm, which solve the problems set forth in the background art.
In order to achieve the above purpose, the present invention provides the following technical solutions: the system comprises an endoscope image receiving unit, an image correcting unit, a color conversion processing unit, an image noise reduction unit, an image sharpening unit, an endoscope external image removing unit, an endoscope position identifying unit, a focus image model library and a focus group extracting unit, wherein the output end of the endoscope image receiving unit is in communication connection with the input end of the image correcting unit, the output end of the image correcting unit is in communication connection with the input end of the color conversion processing unit, the color conversion processing unit is in bidirectional connection with the image noise reduction unit, the image noise reduction unit is in bidirectional connection with the image sharpening unit, the output end of the color conversion processing unit is in communication connection with the input end of the endoscope external image removing unit, the output end of the endoscope external image removing unit is in communication connection with the input end of the endoscope position identifying unit, the endoscope position identifying unit is in bidirectional connection with the focus image model library, and the focus image model library is in bidirectional connection with the focus group extracting unit;
the endoscope image receiving unit is used for collecting and processing images acquired by the endoscope;
the image correction unit is used for correcting the black level detected by the OB pixel;
the color conversion processing unit is used for performing white balance adjustment processing on the endoscope image and performing adjustment processing on the brightness of the endoscope image;
the image noise reduction unit is used for adjusting and repairing noise points in the endoscope image;
the image sharpening unit is used for carrying out graphic image sharpening processing on the obtained endoscope image;
the endoscope external image removing unit is used for identifying and removing external images in the endoscope images and extracting required images;
the endoscope position identification unit is used for identifying the shooting position of the endoscope in real time;
the focus image model library is used for storing and processing image models of focus types;
the focus group extraction unit is used for performing group image extraction processing on a part with a focus in the endoscopic image.
Preferably, the image correction unit comprises an OB black level correction module and a lens shading LSC correction module, wherein the OB black level correction module is in bidirectional connection with the lens shading LSC correction module;
the OB black level correction module is used for performing calibration processing on black level data detected by OB pixels;
the lens shading LSC correction module is used for carrying out calibration processing on lens shading existing in an endoscope shooting image.
Preferably, the color conversion processing unit comprises a white balance module and a gamma correction module, wherein the output end of the white balance module is in communication connection with the input end of the gamma correction module;
the white balance module is used for modifying white balance indexes of white accuracy after the three primary colors of red, green and blue of the endoscope image are mixed and generated;
the gamma correction module is used for correcting the brightness value of the endoscope image.
Preferably, the endoscope external image removing unit comprises an endoscopic image identifying module, an internal image frame selecting module, an internal image contour identifying module and an external image removing module, wherein the output end of the endoscopic image identifying module is in communication connection with the input end of the internal image frame selecting module, the output end of the internal image frame selecting module is in communication connection with the input end of the internal image contour identifying module, and the output end of the internal image contour identifying module is in communication connection with the input end of the external image removing module;
the endoscopic image recognition module is used for carrying out real-time recognition monitoring treatment on the endoscopic image;
the in-vivo image frame selection module is used for carrying out local frame selection processing on in-vivo images shot by the endoscope;
the in-vivo image contour recognition module is used for carrying out contour selection processing on a required in-vivo image;
the in-vitro image removing module is used for removing in-vitro images except the outline after the outline is selected.
Preferably, the endoscope position recognition unit is connected with an endoscope image model library in a bidirectional manner, and the endoscope image model library is used for inputting an image model of an endoscope image reaching a body part and establishing the image model library.
Preferably, the endoscope position recognition unit comprises an endoscope image comparison module, a focus position arrival recognition module and a focus image recognition module, wherein the output end of the endoscope image comparison module is in communication connection with the input end of the focus position arrival recognition module, and the output end of the focus position arrival recognition module is in communication connection with the input end of the focus image recognition module;
the endoscopic image comparison module is used for comparing the obtained endoscopic image with an endoscopic image model library;
the focus position arrival identification module is used for identifying, detecting and processing focus positions reached by the endoscope images monitored in real time;
the focus image recognition module is used for recognizing focus images existing in the focus positions detected and extracting and processing the focus images.
Preferably, the external image removing unit of the endoscope is connected with a pixel value adjusting unit in a bidirectional manner, and the pixel value adjusting unit is used for adjusting the pixel value of the endoscope image.
Preferably, the pixel value adjusting unit comprises a pixel value detecting module and a pixel value average calculating module, wherein the output end of the pixel value detecting module is in communication connection with the input end of the pixel value average calculating module;
the pixel value detection module is used for detecting the pixel value of each part of the endoscopic shot image;
the pixel value average value calculation module is used for calculating the average value of each pixel value in the endoscope image and carrying out overall pixel change processing on the endoscope image according to the calculated average value.
Preferably, the lesion group extracting unit is connected with a disease severity stage identifying unit in two directions, and the disease severity stage identifying unit is used for carrying out stage identification processing on the severity of the disease in the endoscope image according to the lesion image.
An endoscopic image processing algorithm comprising the steps of:
s1, after original RAWDATA data of an image are received, firstly, the RAW format data are converted into RGB format data through OB black level correction and interpolation algorithm;
s2, performing lens shading correction, color space conversion, white balance and gamma correction, noise reduction and sharpening;
s3, converting the RGB format data into YUV format data, and finally outputting the YUV format data to a display for display through HDMI/DVI;
s4, coding the displayed image signals into pictures in a JPEG format and processing video.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Compared with the prior art, the invention has the beneficial effects that: by adding the endoscope position identification unit, the endoscopic image and the focus image are checked, contrast is carried out, the focus image existing in the endoscopic image is identified, so that the endoscopic image is subjected to focus judgment processing, by adding the endoscope external image removal unit, the internal endoscopic image is selected, the images outside the body are deleted, the external image is rapidly removed, the image processing efficiency is improved, and by adding the pixel value adjustment unit, the pixel value of the endoscopic shooting image is adjusted, so that the observation identification degree of the endoscopic image is adjusted, and the observation efficiency of the endoscopic image is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It will be apparent to those skilled in the art from this disclosure that the drawings described below are merely exemplary and that other embodiments may be derived from the drawings provided without undue effort.
FIG. 1 is a block diagram of a system of the present invention;
FIG. 2 is a block diagram of an endoscopic extracorporeal image removal unit system of the present invention;
FIG. 3 is a block diagram of a pixel value adjusting unit system according to the present invention.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus consistent with some aspects of the disclosure as detailed in the accompanying claims.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1:
referring to fig. 1 to 3, an endoscope image processing system according to an embodiment of the present invention includes an endoscope image receiving unit, an image correcting unit, a color conversion processing unit, an image noise reduction unit, an image sharpening unit, an endoscope external image removing unit, an endoscope position identifying unit, a focus image model library and a focus group extracting unit, wherein an output end of the endoscope image receiving unit is in communication connection with an input end of the image correcting unit, an output end of the image correcting unit is in communication connection with an input end of the color conversion processing unit, the color conversion processing unit is in bidirectional connection with the image noise reduction unit, the image noise reduction unit is in bidirectional connection with the image sharpening unit, an output end of the color conversion processing unit is in communication connection with an input end of the endoscope external image removing unit, the endoscope position identifying unit is in bidirectional connection with a focus image model library, and the focus image model library is in bidirectional connection with the focus group extracting unit;
the endoscope image receiving unit is used for collecting and processing images acquired by the endoscope, and receiving and processing the endoscope images through the endoscope, so that the image collecting efficiency is improved;
the image correction unit is used for correcting the black level detected by the OB pixels and processing the OB black level correction and lens shading according to the endoscope image;
the color conversion processing unit is used for performing white balance adjustment processing on the endoscope image and performing adjustment processing on the brightness of the endoscope image;
the image noise reduction unit is used for adjusting and repairing noise points in the endoscope image;
the image sharpening unit is used for carrying out graphic image sharpening processing on the acquired endoscope image;
the endoscope external image removing unit is used for identifying and removing external images in the endoscope images, extracting needed images, selecting internal endoscopic images, deleting images outside the body, rapidly removing the external images, improving the efficiency of image processing, identifying the positions of the endoscopic images, selecting frames of the internal endoscopic images by the internal image features of the endoscopic images, and removing the external endoscopic images after the frames are selected to obtain the internal images;
the endoscope position recognition unit is used for recognizing the shooting position of the endoscope in real time, and by adding the endoscope position recognition unit, the endoscopic image and the focus image are checked, contrast is carried out, and the focus image existing in the endoscopic image is recognized, so that the focus judgment processing is carried out on the endoscopic image;
the focus image model library is used for storing and processing the image model of the focus type;
the focus group extraction unit is used for carrying out group image extraction treatment on the part with focus in the endoscopic image, carrying out group extraction on the focus part and confirming the disease symptoms.
Example 2:
the image correction unit comprises an OB black level correction module and a lens shading LSC correction module, and the OB black level correction module is in bidirectional connection with the lens shading LSC correction module;
the OB black level correction module is used for performing calibration processing on black level data detected by the OB pixels;
the lens shading LSC correction module is used for carrying out calibration processing on lens shading existing in the endoscope shooting image.
The color conversion processing unit comprises a white balance module and a gamma correction module, wherein the output end of the white balance module is in communication connection with the input end of the gamma correction module;
the white balance module is used for modifying white balance indexes of white accuracy after the three primary colors of red, green and blue of the endoscope image are mixed and generated;
the gamma correction module is used for correcting the brightness value of the endoscope image.
The endoscope external image removing unit comprises an endoscopic image identifying module, an internal image frame selecting module, an internal image contour identifying module and an external image removing module, wherein the output end of the endoscopic image identifying module is in communication connection with the input end of the internal image frame selecting module;
the endoscopic image recognition module is used for carrying out real-time recognition monitoring treatment on the endoscopic image;
the in-vivo image frame selection module is used for carrying out local frame selection processing on in-vivo images shot by the endoscope;
the in-vivo image contour recognition module is used for carrying out contour selection processing on a required in-vivo image;
the external image removing module is used for removing the external images outside the outline after the outline is selected, and by adding the endoscope external image removing unit, the internal endoscopic images are selected, the images outside the body are deleted, the external images are rapidly removed, and the image processing efficiency is improved.
The endoscope position recognition unit is connected with an endoscope image model library in a bidirectional mode, and the endoscope image model library is used for inputting an image model of an endoscope image reaching a body part, establishing the image model library, comparing an obtained in-vivo image with a focus characteristic image, and recognizing a focus image existing in the endoscope image.
The endoscope position recognition unit comprises an endoscope image comparison module, a focus position arrival recognition module and a focus image recognition module, wherein the output end of the endoscope image comparison module is in communication connection with the input end of the focus position arrival recognition module;
the endoscopic image comparison module is used for comparing the obtained endoscopic image with the endoscopic image model library;
the focus position arrival identification module is used for identifying, detecting and processing focus positions reached by the endoscope images monitored in real time;
the focus image recognition module is used for recognizing focus images existing in the focus positions detected and extracting and processing the focus images, and by adding the endoscope position recognition unit, the endoscopic images and the focus images are checked and compared, focus images existing in the endoscopic images are recognized, and therefore focus judgment processing is carried out on the endoscopic images.
The endoscopic external image removing unit is connected with a pixel value adjusting unit in a bidirectional mode, the pixel value adjusting unit is used for adjusting the pixel value of the endoscopic image, the pixel point of the endoscopic image is adjusted, and the observation stability and efficiency are improved.
The pixel value adjusting unit comprises a pixel value detecting module and a pixel value average calculating module, wherein the output end of the pixel value detecting module is in communication connection with the input end of the pixel value average calculating module;
the pixel value detection module is used for detecting the pixel value of each part of the endoscopic shot image;
the pixel value average value calculation module is used for calculating the average value of each pixel value in the endoscope image, carrying out overall pixel change processing on the endoscope image according to the calculated average value, and adding the pixel value adjustment unit to realize adjustment of the pixel value of the image shot by the endoscope, so that the observation identification degree of the endoscope image is adjusted, and the observation efficiency of the endoscope image is improved.
The disease severity stage identification unit is used for carrying out stage identification treatment on the severity degree of the disease in the endoscope image according to the focus image, carrying out group extraction on focus positions, confirming disease symptoms and extracting disease information.
Example 3:
an endoscopic image processing algorithm comprising the steps of:
s1, after original RAWDATA data of an image are received, firstly, the RAW format data are converted into RGB format data through OB black level correction and interpolation algorithm;
s2, performing lens shading correction, color space conversion, white balance and gamma correction, noise reduction and sharpening;
s3, converting the RGB format data into YUV format data, and finally outputting the YUV format data to a display for display through HDMI/DVI;
s4, coding the displayed image signals into pictures in a JPEG format and processing video.
Example 4:
the method comprises the steps of receiving an endoscope image through the endoscope, processing OB black level correction and lens shading according to the endoscope image, changing the image color of the endoscope image, performing noise elimination processing on the image, identifying the position of the endoscope image, framing the in-vivo image of the endoscope image by in-vivo image features of the endoscope image, removing the in-vitro endoscope image after framing to obtain an in-vivo image, performing contrast processing on the obtained in-vivo image and focus feature image, identifying focus images existing in the endoscope image, identifying focus images, confirming an endoscope disease, extracting focus positions, confirming disease symptoms, and extracting disease information.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope of the disclosure being indicated by the following claims.
Claims (10)
1. The endoscope image processing system is characterized by comprising an endoscope image receiving unit, an image correcting unit, a color conversion processing unit, an image noise reduction unit, an image sharpening unit, an endoscope external image removing unit, an endoscope position identifying unit, a focus image model library and a focus group extracting unit, wherein the output end of the endoscope image receiving unit is in communication connection with the input end of the image correcting unit, the output end of the image correcting unit is in communication connection with the input end of the color conversion processing unit, the color conversion processing unit is in bidirectional connection with the image noise reduction unit, the image noise reduction unit is in bidirectional connection with the image sharpening unit, the output end of the color conversion processing unit is in communication connection with the input end of the endoscope external image removing unit, the output end of the endoscope external image removing unit is in communication connection with the input end of the endoscope position identifying unit, the endoscope position identifying unit is in bidirectional connection with the focus image model library, and the focus image model library is in bidirectional connection with the focus group extracting unit;
the endoscope image receiving unit is used for collecting and processing images acquired by the endoscope;
the image correction unit is used for correcting the black level detected by the OB pixel;
the color conversion processing unit is used for performing white balance adjustment processing on the endoscope image and performing adjustment processing on the brightness of the endoscope image;
the image noise reduction unit is used for adjusting and repairing noise points in the endoscope image;
the image sharpening unit is used for carrying out graphic image sharpening processing on the obtained endoscope image;
the endoscope external image removing unit is used for identifying and removing external images in the endoscope images and extracting required images;
the endoscope position identification unit is used for identifying the shooting position of the endoscope in real time;
the focus image model library is used for storing and processing image models of focus types;
the focus group extraction unit is used for performing group image extraction processing on a part with a focus in the endoscopic image.
2. The endoscopic image processing system according to claim 1, wherein the image correction unit comprises an OB black level correction module and a lens shading LSC correction module, the OB black level correction module being bi-directionally connected with the lens shading LSC correction module;
the OB black level correction module is used for performing calibration processing on black level data detected by OB pixels;
the lens shading LSC correction module is used for carrying out calibration processing on lens shading existing in an endoscope shooting image.
3. The endoscopic image processing system according to claim 2, wherein the color conversion processing unit comprises a white balance module and a gamma correction module, and an output end of the white balance module is in communication connection with an input end of the gamma correction module;
the white balance module is used for modifying white balance indexes of white accuracy after the three primary colors of red, green and blue of the endoscope image are mixed and generated;
the gamma correction module is used for correcting the brightness value of the endoscope image.
4. An endoscopic image processing system according to claim 3, wherein the endoscopic external image removal unit comprises an endoscopic image recognition module, an internal image frame selection module, an internal image contour recognition module and an external image removal module, wherein an output end of the endoscopic image recognition module is in communication connection with an input end of the internal image frame selection module, an output end of the internal image frame selection module is in communication connection with an input end of the internal image contour recognition module, and an output end of the internal image contour recognition module is in communication connection with an input end of the external image removal module;
the endoscopic image recognition module is used for carrying out real-time recognition monitoring treatment on the endoscopic image;
the in-vivo image frame selection module is used for carrying out local frame selection processing on in-vivo images shot by the endoscope;
the in-vivo image contour recognition module is used for carrying out contour selection processing on a required in-vivo image;
the in-vitro image removing module is used for removing in-vitro images except the outline after the outline is selected.
5. An endoscopic image processing system according to claim 4, wherein the endoscopic position recognition unit is connected with an endoscopic image model library in two directions, and the endoscopic image model library is used for inputting an image model of an endoscopic image reaching a body part and establishing and processing the image model library.
6. The endoscope image processing system according to claim 5, wherein the endoscope position recognition unit comprises an endoscope image comparison module, a focus position arrival recognition module and a focus image recognition module, the output end of the endoscope image comparison module is in communication connection with the input end of the focus position arrival recognition module, and the output end of the focus position arrival recognition module is in communication connection with the input end of the focus image recognition module;
the endoscopic image comparison module is used for comparing the obtained endoscopic image with an endoscopic image model library;
the focus position arrival identification module is used for identifying, detecting and processing focus positions reached by the endoscope images monitored in real time;
the focus image recognition module is used for recognizing focus images existing in the focus positions detected and extracting and processing the focus images.
7. The endoscopic image processing system according to claim 6, wherein the endoscopic external image removal unit is bidirectionally connected with a pixel value adjustment unit for performing adjustment processing on a pixel value of the endoscopic image.
8. The endoscopic image processing system according to claim 7, wherein the pixel value adjusting unit comprises a pixel value detecting module and a pixel value average calculating module, and an output end of the pixel value detecting module is in communication connection with an input end of the pixel value average calculating module;
the pixel value detection module is used for detecting the pixel value of each part of the endoscopic shot image;
the pixel value average value calculation module is used for calculating the average value of each pixel value in the endoscope image and carrying out overall pixel change processing on the endoscope image according to the calculated average value.
9. An endoscopic image processing system according to claim 8, wherein said lesion group extracting unit is bi-directionally connected with a disease severity stage identifying unit for performing stage identification processing on severity of disease in the endoscopic image based on the lesion image.
10. An endoscopic image processing algorithm according to any of claims 1-9, comprising the steps of:
s1, after original RAWDATA data of an image are received, firstly, the RAW format data are converted into RGB format data through OB black level correction and interpolation algorithm;
s2, performing lens shading correction, color space conversion, white balance and gamma correction, noise reduction and sharpening;
s3, converting the RGB format data into YUV format data, and finally outputting the YUV format data to a display for display through HDMI/DVI;
s4, coding the displayed image signals into pictures in a JPEG format and processing video.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310380138.8A CN116369824A (en) | 2023-04-11 | 2023-04-11 | Endoscope image processing system and algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310380138.8A CN116369824A (en) | 2023-04-11 | 2023-04-11 | Endoscope image processing system and algorithm |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116369824A true CN116369824A (en) | 2023-07-04 |
Family
ID=86961330
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310380138.8A Pending CN116369824A (en) | 2023-04-11 | 2023-04-11 | Endoscope image processing system and algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116369824A (en) |
-
2023
- 2023-04-11 CN CN202310380138.8A patent/CN116369824A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US8515141B2 (en) | Medical image processing apparatus and method for detecting locally protruding lesion | |
| US8055033B2 (en) | Medical image processing apparatus, luminal image processing apparatus, luminal image processing method, and programs for the same | |
| US9554097B2 (en) | Endoscope image processing device, endoscope system, and image processing method | |
| CN106102557B (en) | Image processing apparatus | |
| JP4994737B2 (en) | Medical image processing apparatus and medical image processing method | |
| US20100034443A1 (en) | Medical image processing apparatus and medical image processing method | |
| CN104700424B (en) | Medical color fujinon electronic video endoscope dead pixel points of images detection means | |
| US20090196495A1 (en) | Image processing apparatus, image processing method and image processing program | |
| CN107705852A (en) | Real-time the lesion intelligent identification Method and device of a kind of medical electronic endoscope | |
| US20130064436A1 (en) | Medical image processing apparatus and method of operating medical image processing apparatus | |
| KR20200056709A (en) | Method for rendering 3d image, image processing apparatus using said method, camera apparatus interlocking with said image processing apparatus, photographing method of said camera, and 3d image rendering system | |
| CN116703798B (en) | Esophagus multi-mode endoscope image enhancement fusion method based on self-adaptive interference suppression | |
| JP5581237B2 (en) | Image processing device, processor device for electronic endoscope, operation method of image processing device, and computer program for image processing | |
| CN113139937A (en) | Digestive tract endoscope video image identification method based on deep learning | |
| CN110969603B (en) | Relative positioning method and device for lesion position and terminal equipment | |
| JP2010220794A (en) | Device, method, and program for rotating endoscope image | |
| JP5622903B2 (en) | Image processing apparatus, method of operating image processing apparatus, and image processing program | |
| CN116369824A (en) | Endoscope image processing system and algorithm | |
| CN116245771B (en) | Endoscope image brightness automatic enhancement method and system based on image secondary enhancement | |
| US20230007213A1 (en) | Image recording system, image recording method, and recording medium | |
| WO2022095839A1 (en) | Image processing method, and electronic device and readable storage medium | |
| JPH0236836A (en) | Electronic endoscope image processing device | |
| JP5057651B2 (en) | Lumen image processing apparatus, lumen image processing method, and program therefor | |
| CN116269170B (en) | Multi-pixel endoscope self-adaptive recognition host and method | |
| CN115767239B (en) | Image acquisition and processing system, method and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |