A high-definition medical image processing method

Abstract

The present invention relates to the field of image processing technology. Specifically, the present invention relates to a high-definition medical image processing method. The present invention performs noise reduction processing on the ileocecal image through a guided filtering algorithm, obtains the histogram of the ileocecal image, calculates the potential secretion index of each pixel point in the ileocecal image according to the ileocecal histogram, extracts the pixel points exceeding the potential secretion index by setting a segmentation threshold, connects the pixel points to highlight the diseased area of the ileocecal part, and uses an edge detection algorithm to extract the edge features of the diseased area image, obtains the resolution of the edge pixel points, then uses Fourier transform analysis to obtain the blur degree coefficient of the edge pixel points, obtains the enhancement coefficient of the diseased area image according to the resolution and blur degree coefficient of the edge pixel points, and realizes the enhancement of the diseased area image of the ileocecal part by adjusting the enhancement coefficient of the diseased area image.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and more specifically, to a high-definition medical image processing method. Background Art

[0002] Image processing is an important technology. With the continuous progress of image processing technology, especially the development of deep learning algorithms, it is possible to detect and identify minute lesions and early lesions more accurately, improve the accuracy of disease diagnosis, formulate personalized treatment plans for each patient by analyzing the medical images of patients in detail, improve the treatment effect, utilize image processing to analyze a large amount of medical image data, mine potential disease patterns and risk factors, and achieve early prediction and prevention of diseases. When performing medical image processing, the traditional high-definition medical image processing method has a relatively single processing method and no pertinence. It does not find the lesion area by analyzing the features in the image and perform multi-feature extraction on the lesion area, and does not obtain the enhancement coefficient of the lesion area image by analyzing the multi-features of the lesion area image, so as to realize the enhancement of the lesion area image. To solve this technical problem, we provide a high-definition medical image processing method. Summary of the Invention

[0003] The purpose of the present invention is to provide a high-definition medical image processing method to solve the problems raised in the above background art.

[0004] To achieve the above purpose, the present invention provides a high-definition medical image processing method, including the following steps:

[0005] S1. Use an image acquisition device to obtain a medical image of the ileocecal region, mark it as the ileocecal region image, perform noise reduction processing on the ileocecal region image through a guided filter algorithm, extract the pixel values of the ileocecal region image after the noise reduction processing is completed, and equalize the pixel values of the ileocecal region image to obtain the ileocecal region image histogram;

[0006] S2. Calculate the potential secretion index of each pixel point in the ileocecal region image according to the ileocecal region histogram, set a segmentation threshold, and use the threshold segmentation method to compare and classify the potential secretion index of each pixel point in the ileocecal region image with the segmentation threshold to obtain the pixel points in the ileocecal region image that exceed the segmentation threshold, and form a set of suspected lesion pixel points;

[0007] S3. Extract the pixel points in the set of suspected lesion pixel points as the pixel points of the lesion area, and use an edge detection algorithm to connect the pixel points of the lesion area to form a lesion area image, extract the edge features of the lesion area image, and obtain the resolution of the edge pixel points according to the edge features of the lesion area image;

[0008] S4. Analyze the frequency components of the image of the diseased area using Fourier transform, obtain the blurriness index of the image of the diseased area, and obtain the blurriness coefficient of the edge pixel points according to the blurriness index;

[0009] S5. Obtain the enhancement coefficient of the image of the diseased area according to the resolution and blurriness coefficient of the edge pixel points, and enhance the image of the diseased area in the ileocecal region by adjusting the enhancement coefficient of the image of the diseased area.

[0010] As a further improvement of this technical solution, in S1, the image of the ileocecal region is denoised by the guided filter algorithm, specifically including:

[0011] Randomly select an image from the images of the ileocecal region as the guidance image, denoted as A, and use the image of the ileocecal region to be denoised as the input image, denoted as B. Set the window radius of the guided filter as r and the regularization parameter as α. For each pixel point c in the image of the ileocecal region to be denoised, establish a window D centered on it e , in the window D e , the output F c of the guided filter is calculated by the following formula, that is:

[0012] F c = a e * A c + b e

[0013] where, a e and b e are obtained by linear regression calculation, A c represents the pixel value of the guidance image, and F c represents the image of the ileocecal region after completing the guided filter denoising.

[0014] As a further improvement of this solution, in S2, the potential secretion index of the ileocecal region is calculated according to the ileocecal histogram, specifically including:

[0015] Assume that the intensity value range of the image F c (x, y) of the ileocecal region after completing the denoising process is [0, L - 1], and its size is M * N. The L represents the total number of color depths in the image of the ileocecal region. The intensity histogram H(k) is calculated using the following formula, that is:

[0016]

[0017] where, represents the gray value of the image F c at the coordinate (x, y), represents the Kronecker function, where 0 ≤ k ≤ L - 1, and the Kronecker function is defined as:

[0018] Extract the skewness and kurtosis of the intensity histogram H(k), and establish a linear model based on the skewness and kurtosis of the intensity histogram H(k) to calculate the potential secretion index γ of each pixel point. Then, the expression of the potential secretion index γ of each pixel point is as follows:

[0019] γ = ω 1 *PD H(k) + ω 2 *FD H(k)

[0020] Where ω 1 and ω 2 are the weight coefficients affecting the potential secretion index γ of each pixel point obtained from experimental data. PD represents the skewness of the intensity histogram H(k), and FD represents the kurtosis of the intensity histogram H(k).

[0021] As a further improvement of this technical solution, in step S2, the potential secretion index of each pixel point in the ileocecal image is compared and classified with the segmentation threshold, specifically as follows:

[0022] Set the secretion index threshold. The potential secretion index at the lesion site is higher than that in the surrounding area. Compare the potential secretion index of each pixel point in the ileocecal image with the secretion index threshold, screen out the pixel points in the ileocecal image whose potential secretion index is higher than the segmentation threshold, and form a set of suspected lesion pixel points from the pixel points in the ileocecal image that exceed the segmentation threshold.

[0023] As a further improvement of this technical solution, in step S3, the pixel points in the lesion area are connected to form a lesion area image, specifically including:

[0024] Use the edge detection algorithm to calculate the gradient magnitude and direction of each pixel point in the lesion area, perform non-maximum suppression in the gradient direction, and set high and low thresholds. According to the high and low thresholds, divide the gradient magnitude into two categories: strong edges and weak edges, and track the strong edge pixel points. Divide the weak edge pixel points adjacent to the strong edge pixel points into edge pixel points for connection to form a lesion area image.

[0025] As a further improvement of this technical solution, in step S3, obtain the resolution of the edge pixel points according to the edge characteristics of the lesion area image, specifically including:

[0026] Extract the coordinates (x i , y i) where \(i\) is the index of the edge pixel. Assume that the actual distance represented by each pixel in the horizontal direction is \(\Delta x\), and the actual distance represented by each pixel in the vertical direction is \(\Delta y\). Convert the coordinates of the edge pixel points from pixel units to actual space units, that is:

[0027] X i = x i * \(\Delta x\)

[0028] Y i = y i * \(\Delta y\)

[0029] By calculating the distance between edge pixel points and analyzing the edge features, the Euclidean distance between the edge pixel points \((X\) i , \(Y\) i ) and \((X\) j , \(Y\) j ) is:

[0030]

[0031] By calculating the minimum distance between edge pixel points, the resolution of the edge pixel points is obtained. The calculation formula for the resolution of the edge pixel points is:

[0032]

[0033] where \(min(d\) ij ) represents the minimum distance between all edge pixel points.

[0034] As a further improvement of this technical solution, in step S4, the frequency components of the lesion area image are analyzed by Fourier transform to obtain the blurriness index of the lesion area image, which specifically includes:

[0035] Convert the image of the lesion area into a grayscale image, and perform smoothing processing on the grayscale image through Gaussian filtering. Define the grayscale image as \(h(x', y')\), and apply two-dimensional fast Fourier transform to the grayscale image, that is:

[0036] \(f(u, v)=f'\{h(x', y')\}\)

[0037] where \(f(u, v)\) is the frequency domain representation, \((u, v)\) is the frequency coordinate. Calculate the spectral amplitude \(S(u, v)\) according to \(f(u, v)\). The spectral amplitude \(S(u, v)\) represents the change intensity of each frequency component \((u, v)\) in the frequency domain, reflecting the energy contribution of these frequencies in the original image. The expression of the spectral amplitude \(S(u, v)\) is:

[0038] \(S(u, v)=|f(u, v)|\)

[0039] According to the spectral amplitude, the ambiguity index is defined by using the energy ratio of the high-frequency component to the low-frequency component. The expression of the ambiguity index is as follows:

[0040]

[0041] Where W represents the index set of the high-frequency component, G represents the index set of the low-frequency component, and the index set of the high-frequency component represents the information of details, edges, and noises in the image. Then, the ambiguity degree coefficient of the edge pixel points is defined as: R = 1 - Q. If the value of R is close to 1, it indicates that the pixel point is clear; otherwise, it indicates that the pixel point is blurred.

[0042] As a further improvement of this technical solution, in step S5, the enhancement coefficient of the lesion area image is obtained according to the resolution and ambiguity degree coefficient of the edge pixel points, which specifically includes:

[0043] It is set that the enhancement coefficient T is inversely proportional to the ambiguity degree coefficient R of the edge pixel points and directly proportional to the resolution of the edge pixel points. Then, the expression of the enhancement coefficient T is:

[0044] T = t * (1 - R) * δ

[0045] Where t is a proportional constant used to control the enhancement degree. The pixel value of the lesion area image is adjusted using the above enhancement coefficient T. Take the pixel value P(x i , y i ) at the position (x i , y i ) of the lesion area image. By calculating the product of the difference between this pixel value and the background pixel value P’(x i , y i ) and the enhancement coefficient T, the enhanced part is obtained, and the enhanced part is added back to the original pixel value P(x i , y i ) to obtain the enhanced pixel value P ZQ (x i , y i ). The expression of the enhanced pixel value P ZQ (x i , y i ) is as follows:

[0046] P ZQ (x i , y i ) = P(x i , y i ) + T * [(P(x i , y i )) - P’(x i , y i )]

[0047] Enhance the details and brightness of the image by increasing the contrast with the background to achieve the enhancement of the image in the ileocecal lesion area.

[0048] The second object of the present invention is to provide a system for implementing a high-definition medical image processing method, which is characterized by including:

[0049] The acquisition and processing unit is used to obtain the medical image of the ileocecal part, and preprocess the ileocecal image through a guided filtering algorithm to obtain the ileocecal image histogram;

[0050] The lesion analysis unit includes a calculation and analysis module and a lesion generation module;

[0051] The calculation and analysis module is used to calculate the potential secretion of each pixel point according to the ileocecal image histogram, set the secretion index threshold, and use the threshold segmentation method for comparison and classification to form a set of lesion pixel points;

[0052] The lesion generation module uses an edge detection algorithm to connect the pixel points in the lesion area to form an image of the lesion area, and obtains the resolution of the edge pixel points according to the edge characteristics of the lesion area image;

[0053] The fuzzy evaluation unit uses Fourier transform to analyze the frequency components of the lesion area image, obtains the blurriness index of the lesion area image, and obtains the blurriness degree coefficient of the edge pixel points according to the blurriness index;

[0054] The enhancement determination unit obtains the enhancement coefficient of the lesion area image according to the resolution and blurriness degree coefficient of the edge pixel points, and realizes the enhancement of the lesion area image in the ileocecal part by adjusting the enhancement coefficient of the lesion area image

[0055] Compared with the prior art, the beneficial effects of the present invention:

[0056] In a high-definition medical image processing method, the ileocecal image is denoised by a guided filtering algorithm to obtain the ileocecal image histogram, which can better preserve the edge information of the image, help clearly present the anatomical structure of the ileocecal region after denoising, and then use the threshold segmentation method to compare and classify the potential secretion index of each pixel point in the ileocecal image with the segmentation threshold, obtain the pixel points in the ileocecal image that exceed the segmentation threshold, thereby reducing the data volume of subsequent processing, improving the processing efficiency, and obtaining the resolution of the edge pixel points according to the edge characteristics of the lesion area image, which can more accurately determine the boundary of the lesion area, help accurately evaluate the size of the lesion, use Fourier transform to analyze the frequency components of the lesion area image, obtain the blurriness index of the lesion area image, and obtain the blurriness degree coefficient of the edge pixel points according to the blurriness index, so as to comprehensively evaluate the overall blurriness characteristics of the lesion area, not limited to local pixels, and obtain the enhancement coefficient of the lesion area image according to the resolution and blurriness degree coefficient of the edge pixel points to achieve the enhancement of the lesion area image. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 is the overall workflow diagram of the present invention;

[0058] Figure 2 is the overall structural schematic diagram of the present invention;

[0059] The meanings of the various reference numerals in the figure are as follows:

[0060] 1. Acquisition and processing unit; 2. Lesion analysis unit; 21. Calculation and analysis module; 22. Lesion generation module; 3. Blur evaluation unit; 4. Enhancement determination unit. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0061] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0062] Embodiment 1

[0063] Please refer to Figure 1 - Figure 2 as shown, this embodiment provides a high-definition medical image processing method, including the following steps:

[0064] S1. Use an image acquisition device to obtain a medical image of the ileocecal region, mark it as the ileocecal region image, and perform noise reduction on the ileocecal region image through the guided filter algorithm. Extract the pixel values of the ileocecal region image after noise reduction, and equalize the pixel values of the ileocecal region image to obtain the ileocecal region image histogram, improving the visibility and details of the ileocecal region image, enhancing the contrast of the image, and making the hidden structures in the ileocecal region image more clearly visible;

[0065] In S1, the noise reduction of the ileocecal region image through the guided filter algorithm specifically includes:

[0066] Randomly select an image from the ileocecal region image as the guidance image, denoted as A, and use the ileocecal region image to be denoised as the input image, denoted as B. Set the window radius of the guided filter as r and the regularization parameter as α. For each pixel point c in the ileocecal region image to be denoised, establish a window D centered on it e , in the window D e , the output F of the guided filter c is calculated through the following formula, that is:

[0067] F c =a e *A c +b e

[0068] where a e and b e are obtained through linear regression calculation, A c represents the pixel value of the guidance image, and F c represents the ileocecal region image after completing the guided filter denoising.

[0069]

[0070] b e =B’-a e *A’

[0071] where J represents the number of pixels in the window J centered on pixel c, which is used to average the calculation results within the window to obtain a representative statistic. A’ represents the mean value of the guidance image A within the window J, reflecting the average brightness or intensity level of the guidance image within the window. B’ represents the mean value of the input image B within the window J, represents the variance of the guidance image A within the window J, which is used to measure the degree of dispersion or the width of the distribution of the pixel values of the guidance image within the window. a e represents the coefficient calculated through the statistical information within the window, which is used to determine the linear relationship between the output image and the guidance image. b e represents another coefficient calculated through the statistical information within the window, which is related to a eTogether determine the final value of the output image. α is expressed as a regularization parameter, which is used to prevent instability caused by a zero denominator or an overly small value, and also plays a role in controlling the filtering effect to avoid over-smoothing or overfitting.

[0072] S2. Calculate the potential secretion index of each pixel point in the ileocecal image based on the ileocecal histogram, set a segmentation threshold, and use the threshold segmentation method to compare and classify the potential secretion index of each pixel point in the ileocecal image with the segmentation threshold to obtain the pixel points in the ileocecal image that exceed the segmentation threshold, which form a set of suspected lesion pixel points;

[0073] In S2, the potential secretion index of the ileocecal part is calculated based on the ileocecal histogram, specifically including:

[0074] Let the ileocecal image F c after noise reduction processing, the intensity value range of (x, y) is [0, L - 1], and its size is M * N. L represents the total number of color depths in the ileocecal image. Use the following formula to calculate the intensity histogram H(k), that is:

[0075]

[0076] Among them, represents the gray value of the image F c at the coordinate (x, y), represents the Kronecker function, 0 ≤ k ≤ L - 1, and the Kronecker function is defined as:

[0077] Because ileocecal secretions usually have an obvious difference in intensity from the surrounding tissues, the intensity of potential secretions usually has a certain contrast with the surrounding tissues in the image. By analyzing the histogram, it can be determined which intensity values represent potential secretions. Extract the skewness and kurtosis of the intensity histogram H(k), and establish a linear model based on the skewness and kurtosis of the intensity histogram H(k) to calculate the potential secretion index γ of each pixel point. Then the expression of the potential secretion index γ of each pixel point is:

[0078] γ = ω 1 *PD H(k) + ω 2 *FD H(k)

[0079] Among them, ω 1 and ω 2The weight coefficients of the potential secretion index γ affecting each pixel obtained from experimental data, where PD represents the skewness of the intensity histogram H(k) and FD represents the kurtosis of the intensity histogram H(k). Calculating the potential secretion index for each pixel based on the intensity histogram can provide a solid foundation for subsequent medical image analysis, thereby improving the accuracy and reliability of diagnosis.

[0080] In S2, the potential secretion index of each pixel in the ileocecal image is compared and classified with the segmentation threshold as follows:

[0081] The ileocecal part of the human intestine produces secretions to protect the inner wall of the intestine from irritation and damage. When the ileocecal part is diseased, the human body mechanism produces a large amount of secretions for self-protection. The large amount of secretions produced at the diseased part of the ileocecal will spread in the intestine, resulting in a difference in the potential secretion index. The potential secretion index at the diseased part is higher than that of the surrounding area. Set the secretion index threshold. The potential secretion index at the diseased part is higher than that of the surrounding area, and the potential secretion index of each pixel in the ileocecal image is compared with the secretion index threshold. The pixels with a potential secretion index higher than the segmentation threshold in the ileocecal image are screened out, and the pixels exceeding the segmentation threshold in the ileocecal image are grouped into a set of suspected lesion pixels, so as to efficiently and accurately identify and screen out potential ileocecal lesion areas, which not only improves the efficiency of medical image analysis.

[0082] S3. Extract the pixels in the set of suspected lesion pixels as the pixels of the lesion area, and use the edge detection algorithm to connect the pixels of the lesion area to form a lesion area image. Extract the edge features of the lesion area image, and obtain the resolution of the edge pixels according to the edge features of the lesion area image;

[0083] In S3, the pixels of the lesion area are connected to form a lesion area image, which specifically includes:

[0084] Use the edge detection algorithm to calculate the gradient magnitude and direction of each pixel in the lesion area, perform non-maximum suppression in the gradient direction, and set high and low thresholds. According to the high and low thresholds, divide the gradient magnitude into two categories: strong edges and weak edges, and track the strong edge pixels. The weak edge pixels adjacent to the strong edge pixels are classified as edge pixels and connected to form a lesion area image.

[0085] In S3, obtaining the resolution of the edge pixels according to the edge features of the lesion area image specifically includes:

[0086] The edge pixels represent the boundary between the lesion area and the background in the image. By obtaining the coordinates of the edge pixels, the specific boundary of the suspected lesion can be accurately located and defined. Therefore, the coordinates (x i , y i ) of all edge pixels in the lesion area image are extracted, where i is the index of the edge pixel. In medical image analysis, the measurement of actual spatial units can provide more intuitive and understandable results. Doctors use actual units to evaluate the size and location of lesions. Therefore, converting pixel units to actual spatial units makes the interpretation of the results clearer. Thus, it is set that the actual distance represented by each pixel in the horizontal direction is Δx, and the actual distance Δy represented by each pixel in the vertical direction. The coordinates of the edge pixels are converted from pixel units to actual spatial units, that is:

[0087] X i = x i *Δx

[0088] Y i = y i *Δy

[0089] By calculating the distances between edge pixels and analyzing the edge features, the Euclidean distance between the edge pixels (X i , Y i ) and (X j , Y j ) is:

[0090]

[0091] By calculating the minimum distance between edge pixels, the resolution of the edge pixels is obtained to help determine the clarity of details in the image. The calculation formula for the resolution of the edge pixels is:

[0092]

[0093] where min(d ij ) represents the minimum distance between all edge pixels.

[0094] S4. Analyze the frequency components of the lesion area image using Fourier transform to obtain the blurriness index of the lesion area image, and obtain the blurriness degree coefficient of the edge pixels according to the blurriness index.

[0095] In S4, analyzing the frequency components of the lesion area image using Fourier transform to obtain the blurriness index of the lesion area image specifically includes:

[0096] Different frequency components in the image correspond to different features. For example, high-frequency components usually represent edges and details, while low-frequency components represent the general shape and background. Through Fourier transform, the frequency characteristics of the image can be better understood, and the blurriness of the lesion area can be quantified by analyzing its frequency components. Blurred composite images usually lose high-frequency information. Therefore, a blurriness index can be extracted through frequency-domain analysis to assist in understanding the nature of the lesion. Therefore, the image of the lesion area is converted into a grayscale image, and the grayscale image is smoothed by Gaussian filtering. The grayscale image is defined as h(x’, y’), and a two-dimensional fast Fourier transform is applied to the grayscale image, that is:

[0097] f(u, v) = f'{h(x’, y’)}

[0098] where f(u, v) is the frequency-domain representation, (u, v) are the frequency coordinates. According to f(u, v), the spectral amplitude S(u, v) is calculated. The spectral amplitude S(u, v) represents the change intensity of each frequency component (u, v) in the frequency domain and reflects the energy contribution of these frequencies in the original image. The expression of the spectral amplitude S(u, v) is:

[0099] S(u, v) = |f(u, v)|

[0100] According to the spectral amplitude, the blurriness index is defined using the energy ratio of high-frequency components to low-frequency components. The expression of the blurriness index is:

[0101]

[0102] where W represents the index set of high-frequency components, G represents the index set of low-frequency components. The index set of high-frequency components represents the information of details, edges, and noise in the image; the index set of low-frequency components represents the information of illumination and color changes in the image. The blurriness index Q reflects the relative energy relationship between low-frequency and high-frequency components. When the Q value is small, it indicates that the high-frequency components are relatively strong and the image is clear;

[0103] then the blurriness degree coefficient of the edge pixel points is defined as: R = 1 - Q. If the value of R is close to 1, it indicates that the pixel point is clear; otherwise, it indicates that the pixel point is blurred.

[0104] S5. Obtain the enhancement coefficient of the lesion area image according to the resolution and blurriness degree coefficient of the edge pixel points, and enhance the image of the ileocecal lesion area by adjusting the enhancement coefficient of the lesion area image.

[0105] In S5, obtaining the enhancement coefficient of the lesion area image according to the resolution and blurriness degree coefficient of the edge pixel points specifically includes:

[0106] Set the enhancement coefficient T to be inversely proportional to the blurring degree coefficient R of the edge pixel points and directly proportional to the resolution of the edge pixel points. Then the expression for the enhancement coefficient T is:

[0107] T = t * (1 - R) * δ

[0108] Where t is a proportionality constant used to control the enhancement degree. Use the above enhancement coefficient T to adjust the pixel values of the lesion area image. Take the pixel value P(x i , y i ) of the lesion area image at the position (x i , y i ). By calculating the product of the difference between this pixel value and the background pixel value P’(x i , y i ) and the enhancement coefficient T, the enhanced part is obtained. The background pixel value is obtained by the neighborhood averaging method, which is a prior art and will not be elaborated here. Then add the enhanced part back to the original pixel value P(x i , y i ) to get the enhanced pixel value P ZQ (x i , y i ). The expression for the enhanced pixel value P ZQ (x i , y i ) is:

[0109] P ZQ (x i , y i ) = P(x i , y i ) + T * [(P(x i , y i )) - P’(x i , y i )]

[0110] Enhance the details and brightness of the image by increasing the contrast with the background, thereby highlighting the lesion area and achieving the enhancement of the image of the ileocecal lesion area.

[0111] In the present invention, the guided filtering algorithm is used to reduce the noise of the ileocecal image while effectively preserving the edges and details of the image. Then, pixel values of the denoised ileocecal image are extracted. By equalizing the pixel values of the ileocecal image, the histogram of the ileocecal image is obtained, and the contrast is enhanced. According to the histogram of the ileocecal image, the potential secretion index of each pixel point in the ileocecal image is calculated. The potential secretion index of each pixel point is classified by comparison using the threshold segmentation method to form a set of suspected lesion pixel points. The pixel points in the lesion area are connected to form a lesion area image. The edge detection method is used to extract the edge features of the lesion area image to obtain the resolution of the edge pixel points. Then, the blurriness index of the lesion area image is analyzed by Fourier transform, and the blurriness coefficient of the edge pixel points is calculated according to the blurriness index. The resolution and blurriness coefficient of the edge pixel points are used to obtain the enhancement coefficient of the lesion area image, and the enhancement of the ileocecal lesion area image is achieved by adjusting the enhancement coefficient of the lesion area image.

[0112] Embodiment 2

[0113] The second object of the present invention is to provide a system for implementing the high-definition medical image processing method described in any one of the above, including:

[0114] The acquisition and processing unit 1 is used to obtain the medical image of the ileocecal part and preprocess the ileocecal image through the guided filtering algorithm to obtain the histogram of the ileocecal image;

[0115] The lesion analysis unit 2 includes a calculation and analysis module 21 and a lesion generation module 22;

[0116] The calculation and analysis module 21 is used to calculate the potential secretion of each pixel point according to the histogram of the ileocecal image, set the secretion index threshold, and perform comparison and classification using the threshold segmentation method to form a set of lesion pixel points;

[0117] The lesion generation module 22 uses the edge detection algorithm to connect the pixel points in the lesion area to form a lesion area image, and obtains the resolution of the edge pixel points according to the edge features of the lesion area image;

[0118] The blur evaluation unit 3 uses Fourier transform to analyze the frequency components of the lesion area image to obtain the blurriness index of the lesion area image, and obtains the blurriness coefficient of the edge pixel points according to the blurriness index;

[0119] The enhancement determination unit 4 obtains the enhancement coefficient of the lesion area image according to the resolution and blurriness coefficient of the edge pixel points, and realizes the enhancement of the ileocecal lesion area image by adjusting the enhancement coefficient of the lesion area image.

[0120] The foregoing has shown and described the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and the descriptions in the specification are only preferred examples of the present invention, and are not used to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of the present invention claimed is defined by the appended claims and their equivalents.

Claims

1. A high-definition medical image processing method, characterized in that: The following steps are involved: S1, using an image acquisition device to obtain a medical image of the ileocecal region, marking it as an ileocecal region image, and performing noise reduction processing on the ileocecal region image through a guided filtering algorithm, extracting pixel values ​​of the ileocecal region image after the noise reduction processing, and equalizing the pixel values ​​of the ileocecal region image to obtain an ileocecal region image histogram; S2, calculating the potential secretion index of each pixel in the ileocecal image according to the ileocecal histogram, setting a segmentation threshold, and using the threshold segmentation method to compare and classify the potential secretion index of each pixel in the ileocecal image with the segmentation threshold, and obtaining the pixels in the ileocecal image that exceed the segmentation threshold to form a set of lesion pixels; S3, extracting pixel points from the set of suspected lesion pixel points as pixel points of the lesion area, and connecting the pixel points of the lesion area using an edge detection algorithm to form an image of the lesion area, extracting edge features of the lesion area image, and obtaining the resolution of edge pixel points according to the edge features of the lesion area image; S4, using Fourier transform to analyze the frequency components of the lesion area image, obtain the blur index of the lesion area image, and obtain the blur degree coefficient of the edge pixel point according to the blur index. In S4, using Fourier transform to analyze the frequency components of the lesion area image, obtain the blur index of the lesion area image, specifically including: The image of the lesion area is converted into a grayscale image, and the grayscale image is smoothed by Gaussian filtering. The grayscale image is defined as h(x', y'), and a two-dimensional fast Fourier transform is applied to the grayscale image, namely: f(u,v)=f'{h(x',y')} Among them, f(u, v) is the frequency domain representation, (u, v) is the frequency coordinate, and the spectrum amplitude S(u, v) is calculated according to f(u, v). The spectrum amplitude S(u, v) represents the change intensity of each frequency component (u, v) in the frequency domain, reflecting the energy contribution of these frequencies in the original image. The expression of the spectrum amplitude S(u, v) is: S(u,v)=|f(u,v)| According to the spectrum amplitude, the ambiguity index is defined by using the energy ratio of the high-frequency component to the low-frequency component. The expression of the ambiguity index is: Wherein, W represents the index set of high-frequency components, G represents the index set of low-frequency components, and the index set of high-frequency components represents the information of details, edges and noise in the image. Then, the blur coefficient of edge pixels is defined as: R=1-Q. If the value of R is close to 1, it indicates that the pixel is clear, otherwise, it indicates that the pixel is blurred. S5. deriving an enhancement coefficient of the lesion region image according to the resolution and blur coefficient of the edge pixel points, and enhancing the ileocecal lesion region image by adjusting the enhancement coefficient of the lesion region image.

2. The high-definition medical image processing method according to claim 1, characterized in that: In S1, the ileocecal image is subjected to noise reduction processing by using a guided filtering algorithm, which specifically includes: A random image is selected from the ileocecal image as the guide image, denoted as A, and the ileocecal image to be denoised is used as the input image, denoted as B. The window radius of the guide filter is set to r, the regularization parameter is α, and for each pixel c in the ileocecal image to be denoised, a window D is established with it as the center. e , in window D e In the example, the output of the guided filter F c The calculation is done by the following formula, namely: F c =a e *A c +b e Among them, a e and b e is calculated by linear regression, A c represents the pixel value of the guidance image, F c Represents the ileocecal image after guided filtering denoising.

3. The high-definition medical image processing method according to claim 2, characterized in that: The step S2 calculates the potential secretion index of the ileocecal region according to the ileocecal region histogram, specifically comprising: Suppose the ileocecal image F after noise reduction is c The intensity value range of (x, y) is [0, L-1], and its size is M*N, where L represents the total number of color depths in the ileocecal image. The intensity histogram H(k) is calculated using the following formula, namely: in, Represented as image F c The gray value at the coordinate (x, y), k is a discrete value within the range of the ileocecal image intensity value, Expressed as Kronecker function, 0≤k≤L-1, the Kronecker function is defined as: The skewness and kurtosis of the intensity histogram H(k) are extracted, and a linear model is established based on the skewness and kurtosis of the intensity histogram H(k) to calculate the potential secretion index γ of each pixel. The expression of the potential secretion index γ of each pixel is: γ=ω1*PD H(k) +ω2*FD H(k) Among them, ω1 and ω2 are the weight coefficients of the potential secretion index γ affecting each pixel obtained through experimental data, PD is expressed as the skewness of the intensity histogram H(k), and FD is expressed as the kurtosis of the intensity histogram H(k).

4. The high-definition medical image processing method according to claim 3, characterized in that: In S2, the potential secretion index of each pixel in the ileocecal image is compared with the segmentation threshold and classified as follows: The secretion index threshold is set, the potential secretion index of the lesion is higher than that of the surrounding area, and the potential secretion index of each pixel in the ileocecal image is compared with the secretion index threshold, the pixels in the ileocecal image whose potential secretion index is higher than the segmentation threshold are screened out, and the pixels in the ileocecal image that exceed the segmentation threshold are grouped into a set of suspected lesion pixels.

5. The high-definition medical image processing method according to claim 1, characterized in that: The pixel points of the lesion area are connected in S3 to form an image of the lesion area, which specifically includes: The edge detection algorithm is used to calculate the gradient amplitude and direction of each pixel in the lesion area, non-maximum suppression is performed in the gradient direction, and high and low thresholds are set. According to the high and low thresholds, the gradient amplitude is divided into strong edge and weak edge categories, and the strong edge pixels are tracked. The weak edge pixels adjacent to the strong edge pixels are divided into edge pixels and connected to form an image of the lesion area.

6. The high-definition medical image processing method according to claim 1, characterized in that: The step S3 of obtaining the resolution of edge pixels according to the edge features of the lesion area image specifically includes: Extract the coordinates of all edge pixels in the lesion area image (x i ,y i ), i is the index of the edge pixel, the actual distance represented by each pixel in the horizontal direction is set to Δx, the actual distance represented by each pixel in the vertical direction is set to Δy, and the coordinates of the edge pixel points are converted from pixel units to actual space units, that is: X i =x i *Δx Y i =y i *Δy By calculating the distance between edge pixels and analyzing edge features, the edge pixel (X i , Y i ) and (X j , Y j ) is: The resolution of the edge pixels is obtained by calculating the minimum distance between the edge pixels. The calculation formula for the resolution of the edge pixels is: Among them, min(d ij ) represents the minimum distance between all edge pixels.

7. The high-definition medical image processing method according to claim 1, characterized in that: The enhancement coefficient of the lesion area image is obtained according to the resolution and blur coefficient of the edge pixel points in S5, which specifically includes: The enhancement coefficient T is set to be inversely proportional to the blur coefficient R of the edge pixel point and proportional to the resolution of the edge pixel point. The expression of the enhancement coefficient T is: T=t*(1-R)*δ Among them, t is a proportional constant used to control the degree of enhancement. The pixel value of the lesion area image is adjusted using the enhancement coefficient T. The lesion area image is taken at position (x i ,y i )’s pixel value P(x i ,y i ), by calculating the pixel value and the background pixel value P'(x i ,y i The product of the difference between the two values ​​and the enhancement coefficient T is used to obtain the enhanced part, and the enhanced part is added back to the original pixel value P(x i ,y i ), and the enhanced pixel value P is obtained ZQ (x i ,y i ), the enhanced pixel value P ZQ (x i ,y i ) is: P ZQ (x i ,y i )=P(x i ,y i )+T*[(P(x i ,y i ))-P’(x i ,y i )] The image details and brightness are enhanced by increasing the contrast with the background, thereby enhancing the image of the ileocecal lesion area.

8. A system for implementing a high-definition medical image processing method according to any one of claims 1 to 7, characterized in that: include: The acquisition and processing unit (1) is used to acquire a medical image of the ileocecal region and pre-process the ileocecal region image by using a guided filtering algorithm to obtain an ileocecal region image histogram; The lesion analysis unit (2) comprises a calculation and analysis module (21) and a lesion generation module (22); The calculation and analysis module (21) is used to calculate the potential secretion of each pixel point according to the ileocecal image histogram, set the secretion index threshold, and use the threshold segmentation method to perform comparison and classification to form a set of pathological pixel points; The lesion generation module (22) connects the pixel points of the lesion area using an edge detection algorithm to form an image of the lesion area, and obtains the resolution of the edge pixel points according to the edge features of the lesion area image; The fuzzy evaluation unit (3) uses Fourier transform to analyze the frequency components of the lesion area image, obtains a fuzziness index of the lesion area image, and obtains a fuzziness coefficient of the edge pixel point based on the fuzziness index; The enhancement determination unit (4) obtains an enhancement coefficient of the lesion region image according to the resolution and blur coefficient of the edge pixel points, and enhances the ileocecal lesion region image by adjusting the enhancement coefficient of the lesion region image.

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