Novel gastrointestinal tract environment image enhancement optimization method

Abstract

The invention discloses a novel gastrointestinal tract environment image enhancement and optimization method. The method comprises the following steps: receiving original image data of a gastrointestinal tract environment; performing real-time shooting by using special medical imaging equipment or obtaining the original image data in other legal modes; analyzing color and contrast information of the original image data on the basis of a preset algorithm: implementing by using multiple algorithms; adjusting image parameters according to the color and contrast information; generating an enhanced gastrointestinal tract environment optimization image; analyzing color and contrast information of the original image data based on a preset algorithm; dynamically adjusting exposure time of an image based on light sensor data of real-time image acquisition equipment so as to stabilize a light environment in gastrointestinal endoscopy. According to the scheme disclosed by the embodiment of the invention, the problem of non-uniform image brightness caused by unstable light environment in gastrointestinal endoscopy can be solved.

Description

Technical Field

[0001] The present invention relates to the field of medical imaging technology, and in particular to a new gastrointestinal environment image enhancement optimization method. Background Art

[0002] The gastrointestinal environment image enhancement optimization method aims to improve the quality of endoscopic images and enhance the accuracy and reliability of medical diagnosis. This method is designed specifically for several common and difficult problems, such as uneven image brightness caused by dynamic changes in the light environment during gastrointestinal endoscopy; unclear display of key anatomical features due to poor imaging angle in complex curved parts of the digestive tract; secretions on the mucosal surface may cause blurring of important details, and during continuous imaging, the patient's natural intestinal peristalsis may cause ghosting of the image; at the same time, in the case of inflammation or bleeding, strong color deviation greatly affects the authenticity of the original color. These problems have significantly increased the difficulty and uncertainty of diagnosis and treatment.

[0003] Therefore, it is urgent to improve this shortcoming. The present invention studies and improves the existing technology and its shortcomings, and provides a new gastrointestinal environment image enhancement optimization method. Summary of the invention

[0004] The purpose of the present invention is to provide a new gastrointestinal environment image enhancement optimization method to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a new gastrointestinal environment image enhancement optimization method, comprising the following steps:

[0006] Receive raw image data of the gastrointestinal environment: by real-time shooting using specialized medical imaging equipment or by other legal means; analyze the color and contrast information of the raw image data based on a preset algorithm: use a variety of algorithms to achieve; adjust image parameters based on the color and contrast information; generate an enhanced optimized image of the gastrointestinal environment; analyze the color and contrast information of the raw image data based on a preset algorithm: dynamically adjust the image exposure time based on the light sensor data of the real-time image acquisition device to stabilize the light environment during gastrointestinal endoscopic examination.

[0007] Furthermore, the multiple algorithms are grayscale algorithm, color space conversion algorithm, and edge detection algorithm.

[0008] Furthermore, according to the output results of the algorithm, various parameters of the image are adjusted accordingly.

[0009] Furthermore, the processed images are synthesized into a final optimized image.

[0010] Furthermore, a histogram equalization step is performed on each received image to improve the overall contrast, and the cumulative distribution function is expressed using the formula \(H(i,j)=\sum_{x=0}^{i}\sum_{y=0}^{j}h(x,y)\) (\(h(x,y)\) is the pixel \((x,y)\) in the original image).

[0011] Furthermore, an optical flow estimation method is used to compare a series of images between frames to identify the displacement caused by intestinal movement and apply compensation.

[0012] Furthermore, a color correction matrix \(C=[c_{ij}]\) is applied, in which the value of each element \(c_{ij}\) is adjusted according to the color deviation of the image area to correct the color shift and distortion caused by inflammation and bleeding.

[0013] Furthermore, a Gaussian filter is used to smooth the noise, and edge enhancement technology is combined to increase detail recognition.

[0014] The present invention provides a new gastrointestinal environmental image enhancement optimization method, which has the following beneficial effects: the present application can solve the problem of uneven image brightness caused by unstable light environment in gastrointestinal endoscopy, and can effectively improve the clarity and contrast of gastrointestinal images, thereby providing a more accurate and reliable basis for clinical diagnosis, and can effectively improve the quality and readability of gastrointestinal environmental images, which helps doctors to more accurately judge the condition and carry out corresponding treatment. At the same time, the implementation process of the present invention is simple and efficient, which can greatly save the time and manpower costs of medical resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 The figure is a schematic flow chart of a novel gastrointestinal environment image enhancement optimization method of the present invention. DETAILED DESCRIPTION

[0016] The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0017] like Figure 1 As shown, a new gastrointestinal environment image enhancement optimization method comprises the following steps:

[0018] Receiving raw image data of the gastrointestinal environment, which may be acquired by real-time photography using specialized medical imaging equipment or by other legal means;

[0019] Analyze the color and contrast information of the original image data based on a preset algorithm. This step can be achieved using a variety of algorithms, such as grayscale, color space conversion, edge detection, etc.;

[0020] Adjusting image parameters according to the color and contrast information. In this step, various parameters of the image may be adjusted accordingly according to the output of the algorithm to achieve the purpose of improving image quality.

[0021] Generate an enhanced gastrointestinal environment optimized image. This step is to synthesize the processed image into a final optimized image;

[0022] Analyzing the color and contrast information of the original image data based on a preset algorithm includes:

[0023] Dynamically adjust the image exposure time based on the light sensor data of the real-time image acquisition device to stabilize the light environment during gastrointestinal endoscopy. This step can effectively prevent the image quality from deteriorating due to light changes.

[0024] A histogram equalization step is performed on each received image to improve the overall contrast, and the cumulative distribution function is expressed using the formula (H(i,j)=\sum_{x=0}^{i}\sum_{y=0}^{j}h(x,y)) ((h(x,y)) is the pixel ((x,y)) in the original image);

[0025] An optical flow estimation method was used to compare a series of images between frames to identify the displacement caused by intestinal movement and apply compensation;

[0026] Applying a color correction matrix (C = [c_{ij}]), where the value of each element (c_{ij}) is adjusted according to the color deviation of the image area to correct the color shift and distortion caused by inflammation and bleeding;

[0027] A Gaussian filter is used to smooth noise, and edge enhancement technology is used to increase detail recognition. The key feature contours on the mucosal surface are highlighted through edge enhancement technology, thereby reducing the unclear details caused by secretion occlusion.

[0028] In order to receive the original image data of the gastrointestinal environment, the image information is captured in real time or in advance during the process of the gastroenteroscopic device acquiring the image information. The image covers the gastrointestinal wall structure and pathological information. In the process of image acquisition, there may be insufficient or over-concentrated light sources, which may affect the imaging effect, especially the tiny structures that are critical to diagnosis, such as the identification of mucosal changes, which brings challenges.

[0029] The images obtained from the gastrointestinal tract are analyzed based on preset algorithms, focusing on their color and contrast properties. In this process, adaptive image enhancement technology combined with deep learning algorithms are used to evaluate the color and brightness distribution of each pixel to determine the highlights, shadows and color deviations in the image. In order to cope with the complex conditions unique to the gastrointestinal tract, an enhanced algorithm model is introduced in this step, which can learn more complex image characteristics, such as color variations of the mucosa or color patterns of inflammatory areas. For example, when analyzing the images, abnormal green shifts may be detected near some inflammatory areas. This atypical color indication may be the result of excessive mucus or bleeding. At this time, the color correction technology of machine learning can identify this deviation and make corresponding plans for subsequent parameter adjustments accordingly.

[0030] Image parameters are adjusted based on the color and contrast information obtained from the analysis. This process includes contrast enhancement, detail highlighting, and color tone standardization. In order to improve the problem of difficult imaging in certain areas of the gastrointestinal tract, a nonlinear image stretching algorithm is used to adjust the brightness of images under low light conditions, and edge detection and enhancement are used to compensate for the loss of key features due to poor angles; at the same time, for blurring caused by mucosal secretions, denoising and sharpening operations are used to make the fine structures in the image clearer. In one embodiment, when a part of the gastrointestinal tract is poorly imaged due to intestinal fluid masking, the method can automatically detect the blurred part and restore it to normal clarity through texture reconstruction technology, so that subtle mucosal lesions can be prominently presented in the picture.

[0031] Generate the final version of the image after a series of complex and precise adjustments - that is, the optimized gastrointestinal environment enhanced image. The optimized image not only has a more accurate representation in terms of brightness, contrast, details and color, but also greatly reduces image artifacts and ghosting, making it easier for medical professionals to identify and diagnose the condition. For example, during the examination, it was found that the rapid movement of the patient's intestinal segments caused a set of images to have slight but clearly visible ghosting, which affected the accuracy of the observation. In the final stage, through the method of time filtering and image fusion, the best quality fragments can be selected between consecutive frames to merge into a complete, clear photo without ghosting interference, which solves the image quality problem existing in gastrointestinal examinations, helps to improve the accuracy and reliability of the examination, and brings greater support and technical assistance to medical diagnosis.

[0032] Dynamically adjust the image exposure time based on the information provided by the light sensor on the real-time image acquisition device. This sensor can monitor light changes in the gastrointestinal tract and automatically or manually adjust the exposure settings of the endoscopic image according to the ambient brightness. This can obtain images under stable lighting conditions under different light conditions and eliminate brightness differences caused by unstable lighting. For example, during gastrointestinal endoscopy, when entering parts with different curvatures may cause significant changes in ambient light intensity, this function will be particularly useful to ensure stable image quality;

[0033] Histogram equalization is applied to each received image to improve the overall contrast and clarity of the image. The formula \(H(i,j)=\sum_{x=0}^{i}\sum_{y=0}^{j}h(x,y)\) defines the cumulative distribution function \(H(i,j)\), which is the result of the accumulation of the probability of the intensity levels of the original image pixels, and \(h(x,y)\) represents the probability of occurrence at the intensity level of the pixel position (x,y). This step helps to enhance the details of the concave and convex parts of the digestive tract. Specifically, in some curved areas with large angle changes or shadows, the application of histogram equalization can significantly improve the imaging effect;

[0034] The next step is to adjust the image parameters based on the quality characteristics of these images according to the information of color and contrast. The image parameters referred to here include adjustment values ​​such as brightness and sharpness. The purpose is to make the details partially obscured by mucus or other secretions reappear and to reduce the ghosting effect caused by organ movement. In the process of adjusting the parameters, special corrections are made for color deviation to ensure that the true color of the tissue can be correctly presented even in cases of inflammation or bleeding. This process aims to balance the brightness and darkness of the picture and improve the overall visual clarity, which is particularly useful when dealing with insufficient brightness or overexposure caused by fluctuations in the light environment.

[0035] The above adjustments ultimately generate an enhanced version of the gastrointestinal environment optimized image, which will show important features more clearly and minimize image problems caused by endoscopic viewing angle limitations and patient physiological movements. This optimization method improves image quality and improves the accuracy of medical workers' recognition of disease symptoms such as bleeding or abnormal proliferation areas, laying a good foundation for more accurate gastrointestinal disease diagnosis and treatment processes. This series of steps solves the series of image quality challenges mentioned above, and can make images more in line with medical diagnosis needs under different gastrointestinal conditions.

[0036] Any process or method description in the flow chart or otherwise described herein can be understood to represent a module, fragment or portion of a code including one or more executable instructions for implementing the steps of a specific logical function or process. And the scope of the preferred embodiment of the present application includes other implementations, in which the functions may not be performed in the order shown or discussed, including in a substantially simultaneous manner or in a reverse order according to the functions involved.

[0037] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, which can be embodied in any computer-readable medium for use by an instruction execution system, apparatus or device (such as a computer-based system, a system including a processor or other system that can fetch instructions from an instruction execution system, apparatus or device and execute instructions), or used in combination with these instruction execution systems, apparatuses or devices.

[0038] It should be understood that the various parts of the present application can be implemented with hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented with software or firmware stored in a memory and executed by a suitable instruction execution system. All or part of the steps of the above embodiment method can be completed by instructing the relevant hardware through a program, which can be stored in a computer-readable storage medium, and when the program is executed, it includes one of the steps of the method embodiment or a combination thereof.

[0039] The embodiments of the present invention are given for the purpose of illustration and description, and are not intended to be exhaustive or to limit the invention to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for specific uses.

Claims

1. A new gastrointestinal environment image enhancement optimization method, characterized in that: The method comprises the following steps: receiving raw image data of the gastrointestinal environment by real-time shooting with a dedicated medical imaging device or by other legal means; analyzing the color and contrast information of the raw image data based on a preset algorithm by using a plurality of algorithms; adjusting image parameters according to the color and contrast information; Generate enhanced images of the gastrointestinal environment; The color and contrast information of the original image data is analyzed based on a preset algorithm: the exposure time of the image is dynamically adjusted based on the light sensor data of the real-time image acquisition device to stabilize the light environment during the gastrointestinal endoscopy.

2. A new gastrointestinal environment image enhancement optimization method according to claim 1, characterized in that: The various algorithms are grayscale algorithm, color space conversion algorithm, and edge detection algorithm.

3. A new gastrointestinal environment image enhancement optimization method according to claim 1, characterized in that: According to the output results of the algorithm, the various parameters of the image are adjusted accordingly.

4. A new gastrointestinal environment image enhancement optimization method according to claim 1, characterized in that: The processed images are synthesized into the final optimized image.

5. A new gastrointestinal environment image enhancement optimization method according to claim 1, characterized in that: A histogram equalization step is performed on each received image to improve the overall contrast, and the cumulative distribution function is expressed using the formula \(H(i,j)=\sum_{x=0}^{i}\sum_{y=0}^{j}h(x,y)\) (\(h(x,y)\) is the pixel \((x,y)\) in the original image).

6. A new gastrointestinal environment image enhancement optimization method according to claim 1, characterized in that: An optical flow estimation method was used to compare a series of images frame by frame to identify the displacement caused by intestinal movement and apply compensation.

7. A new gastrointestinal environment image enhancement optimization method according to claim 5, characterized in that: A color correction matrix \(C=[c_{ij}]\) is applied, in which the value of each element \(c_{ij}\) is adjusted according to the color deviation of the image area to correct the color shift and distortion caused by inflammation and bleeding.

8. A new gastrointestinal environment image enhancement optimization method according to claim 5, characterized in that: Gaussian filter is used to smooth noise, and edge enhancement technology is used to increase detail recognition.

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