CN115063398A - Method and device for processing electronic digestive tract endoscope image and electronic equipment - Google Patents

Abstract

The invention provides a method and a device for processing an electronic digestive tract endoscope image and electronic equipment, wherein the method comprises the following steps: converting a first YUV image acquired by an electronic digestive tract endoscope into a first RGB image; splitting the first RGB image to obtain three single-color channel images, wherein the three single-color channel images comprise: an R channel image, a G channel image, and a B channel image; processing the three single-color channel images by adopting a histogram equalization algorithm to obtain three processed single-color channel images; merging the processed three single-color channel images to obtain a second RGB image; and converting the second RGB image into a second YUV image. The invention has good pertinence to the blood vessel display and meets the use requirement of a user for efficiently identifying the blood vessel according to the endoscope image.

Description

Electronic digestive tract endoscope image processing method, device and electronic equipment

technical field

Embodiments of the present invention relate to the technical field of electronic digestive tract endoscope imaging, and in particular, to a method, a device, and an electronic device for processing images of an electronic digestive tract endoscope.

Background technique

At present, in order to improve the recognition efficiency of various objects in the electronic digestive tract endoscope image, especially the recognition efficiency of blood vessels in the endoscopic image, it is often necessary to enhance the contrast of the electronic digestive tract endoscope image. The main methods are: :

1) The contrast enhancement method based on histogram equalization adjusts the grayscale histogram of the image to an approximately uniform distribution, which increases the dynamic range of pixel grayscale value differences, thereby enhancing the contrast of blood vessels;

2) FICE technology (Flexible Spectral Imaging ColourEnhancement, intelligent color enhancement technology) based on spectral estimation, select the wavelength combination that is conducive to the observation of blood vessels to enhance the contrast between blood vessels and tissues; based on the edge detection method, use the edge detection operator to distinguish the blood vessels and Strengthens the strength and tone of blood vessels;

3) Based on the deep learning method, a large number of data sets are used to learn the mapping relationship between the original image and the target image, so as to obtain the image after blood vessel enhancement.

However, the above-mentioned methods have poor pertinence for vascular imaging, and are especially prone to excessively enhancing the contrast of the surrounding tissue, which makes it difficult for users to identify the vascular images; Meet the needs of users to efficiently identify blood vessels based on endoscopic images, especially for microvessels and venous vessels.

SUMMARY OF THE INVENTION

Embodiments of the present invention provide an electronic digestive tract endoscope image processing method, device, and electronic equipment, so as to solve the problem that the existing electronic digestive tract endoscope image processing method has poor pertinence for blood vessel display, and is difficult to meet the needs of users according to the endoscope. The problem of using the image to efficiently identify the blood vessel needs.

In order to solve the above-mentioned technical problems, the present invention is achieved in this way:

In a first aspect, an embodiment of the present invention provides a method for processing an image of an electronic digestive tract endoscope, including:

converting the first YUV image collected by the electronic digestive tract endoscope into a first RGB image;

Splitting the first RGB image to obtain three single-color channel images, the three single-color channel images include: an R channel image, a G channel image, and a B channel image;

Using the histogram equalization algorithm to process the three single-color channel images respectively to obtain three single-color channel images after processing;

Merging the processed three single-color channel images to obtain a second RGB image;

Converting the second RGB image to a second YUV image.

Optionally,

Convert the first YUV image collected by the electronic digestive tract endoscope into the first RGB image, which includes:

Perform noise reduction processing on the first YUV image, where the noise reduction processing is used to reduce at least one of the following noises: luminance noise and color noise.

Optionally,

The three single-color channel images are respectively processed by the histogram equalization algorithm, and three single-color channel images after processing are obtained, including:

According to each of the single-color channel images, calculating the grayscale distribution data of each of the single-color channel images, and sending the grayscale distribution data to the interactive terminal associated with the user;

receiving a grayscale histogram limit value sent by the interactive terminal, where the limit value corresponds to the single-color channel image;

According to the limit value of the grayscale histogram, a histogram equalization algorithm is used to process the three single-color channel images respectively to obtain three processed single-color channel images;

or,

The three single-color channel images are respectively processed by the histogram equalization algorithm, and three single-color channel images after processing are obtained, including:

obtaining the preset limit value of the grayscale histogram;

According to the limit value of the grayscale histogram, a histogram equalization algorithm is used to process the three single-color channel images respectively to obtain three processed single-color channel images.

Optionally, use a histogram equalization algorithm to process the three single-color channel images respectively, to obtain three processed single-color channel images, including:

Segment each of the single-color channel images according to a preset segmentation rule to obtain a plurality of image segments corresponding to the single-color channel images;

obtaining a grayscale histogram limit value corresponding to the single-color channel image;

The grayscale histogram of each of the image blocks of the single-color channel image is obtained by calculating, and according to the grayscale histogram limit value corresponding to the single-color channel image, for each of the single-color channel image The grayscale histogram of the image block is cut to obtain the grayscale histogram of each of the image blocks after the cropping;

Obtain the grayscale mapping function of each sub-image according to the grayscale histogram of each of the cut image blocks;

For each image block, an interpolation algorithm is used to interpolate the grayscale mapping function of the image blocks adjacent to the image block to obtain the target gray value of each image block;

Enhance the contrast of the image blocks corresponding to the target gray value according to the target gray value to obtain the processed image blocks; merge the processed image blocks according to the preset segmentation rule to obtain the processed image blocks Three single-color channel images after.

Optionally,

According to the limit value of the grayscale histogram corresponding to the single-color channel image, the grayscale histogram of each of the image blocks of the single-color channel image is trimmed to obtain each trimmed grayscale histogram. Grayscale histogram of image blocks, including:

Cropping step: Detecting each gray level in the grayscale histogram of the image block, and for the grayscale level whose detection result is that the corresponding statistical value of the histogram exceeds the limit value of the grayscale histogram, the grayscale level is determined. The histogram statistic value corresponding to the level is trimmed, so that the histogram statistic value after trimming does not exceed the gray-scale histogram limit value;

Allocate the cropped histogram statistical value to other gray levels whose detection result is that the corresponding histogram statistical value does not exceed the grayscale histogram limit value; perform the cropping step until the image block Each gray level in the grayscale histogram does not exceed the limit value of the grayscale histogram, and the grayscale histogram of the cut image block is obtained.

Optionally, the interpolation algorithm is a bilinear difference algorithm.

In a second aspect, an embodiment of the present invention provides an apparatus for processing an image of an electronic digestive tract endoscope, including:

a conversion module for converting the first YUV image collected by the electronic digestive tract endoscope into a first RGB image;

a splitting module, configured to split the first RGB image to obtain three single-color channel images, where the three single-color channel images include: an R-channel image, a G-channel image, and a B-channel image;

a processing module, configured to process the three single-color channel images respectively by using a histogram equalization algorithm to obtain three processed single-color channel images;

a merging module for merging the processed three single-color channel images to obtain a second RGB image;

The conversion module is further configured to convert the second RGB image into a second YUV image.

Optionally,

The processing module is further configured to segment each of the single-color channel images according to a preset segmentation rule to obtain a plurality of image segments corresponding to the single-color channel images;

The processing module is further configured to obtain a grayscale histogram limit value corresponding to the single-color channel image;

The processing module is further configured to calculate and obtain a grayscale histogram of each of the image blocks of the single-color channel image, and calculate the grayscale histogram limit value corresponding to the single-color channel image for the single-color channel image. The grayscale histogram of each of the image blocks of the single-color channel image is trimmed, and the grayscale histogram of each of the image blocks after the trimming is obtained;

The processing module is further configured to obtain a grayscale mapping function of each sub-image according to the cut grayscale histogram of each of the image blocks;

The processing module is further configured to, for each image block, use an interpolation algorithm to interpolate the grayscale mapping function of the image blocks adjacent to the image block to obtain the target gray value of each image block;

The processing module is further configured to enhance the contrast of the image blocks corresponding to the target gray value according to the target gray value to obtain the processed image blocks; merge and process according to the preset segmentation rule After the image is divided into blocks, three single-color channel images are obtained after processing.

In a third aspect, an embodiment of the present invention provides an electronic device, including a processor, a memory, and a program or instruction stored on the memory and executable on the processor, where the program or instruction is processed by the processor When the device is executed, the steps in the method for processing an image of an electronic digestive tract endoscope according to any one of the first aspects are implemented.

In a fourth aspect, an embodiment of the present invention provides a readable storage medium, where a program or an instruction is stored on the readable storage medium, and when the program or instruction is executed by a processor, the implementation is as described in any one of the first aspect The steps in the processing method of electronic digestive tract endoscopy images.

In the embodiment of the present invention, the first YUV image collected by the electronic digestive tract endoscope is converted into the first RGB image; the first RGB image is split, and the histogram equalization algorithm is used to separate the three single-color channel images Processing can avoid the interference of the existence of other color channels when processing one color channel image, thereby avoiding the problem of excessive contrast enhancement of perivascular tissue caused by direct processing of unsplit endoscopic images. It is beneficial to improve the pertinence of vascular imaging; in the embodiment of the present invention, after obtaining the processed three single-color channel images, merging the processed three single-color channel images is conducive to further improving the effect of image processing, so that the processed internal The endoscopic image has high contrast and high definition, which can meet the needs of users to efficiently identify blood vessels according to the endoscopic image, especially the identification of microvessels and venous vessels.

Description of drawings

Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are for the purpose of illustrating preferred embodiments only and are not to be considered limiting of the invention. Also, the same components are denoted by the same reference numerals throughout the drawings. In the attached image:

FIG. 1 is one of the schematic flowcharts of a method for processing an image of an electronic digestive tract endoscope according to an embodiment of the present invention;

2 is a second schematic flowchart of a method for processing an image of an electronic digestive tract endoscope according to an embodiment of the present invention;

3 is a schematic diagram of the principle of a bilinear interpolation algorithm in a method for processing an image of an electronic digestive tract endoscope according to an embodiment of the present invention;

4 is a third schematic flowchart of a method for processing an image of an electronic digestive tract endoscope according to an embodiment of the present invention;

5 is a schematic diagram of the principle of cropping the grayscale histogram of each image block of a single-color channel image;

FIG. 6 is a schematic diagram of the principle of an image processing device of an electronic digestive tract endoscope according to an embodiment of the present invention;

FIG. 7 is a schematic block diagram of an electronic device according to an embodiment of the present invention.

Detailed ways

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. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of 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.

An embodiment of the present invention provides a method for processing an image of an electronic digestive tract endoscope. Referring to FIG. 1 , FIG. 1 is one of the schematic flowcharts of a method for processing an image of an electronic digestive tract endoscope according to an embodiment of the present invention, including:

Step 11: Convert the first YUV image collected by the electronic digestive tract endoscope into a first RGB image;

Step 12: splitting the first RGB image to obtain three single-color channel images, where the three single-color channel images include: an R-channel image, a G-channel image, and a B-channel image;

Step 13: using the histogram equalization algorithm to process the three single-color channel images respectively, to obtain three processed single-color channel images;

Step 14: combine the processed three single-color channel images to obtain a second RGB image;

Step 15: Convert the second RGB image to a second YUV image.

YUV, is a color coding method. Often used in various video processing components. YUV allows for reduced bandwidth for chroma taking into account human perception when encoding photos or videos.

YUV is a type of compiling true-color color space (colorspace). Proper nouns such as Y'UV, YUV, YCbCr, YPbPr can be called YUV, which overlap each other. "Y" represents the brightness (Luminance or Luma), that is, the grayscale value, and "U" and "V" represent the chroma (Chrominance or Chroma), which are used to describe the color and saturation of the image, and are used to specify pixels. s color.

The RGB color mode is a color standard in the industry. It obtains various colors by changing the three color channels of red (R), green (G), and blue (B) and superimposing them on each other. Yes, RGB is the color representing the three channels of red, green and blue. This standard includes almost all the colors that human vision can perceive and is one of the most widely used color systems.

In some embodiments of the present invention, optionally, in step 11, the conversion formula for converting the first YUV image to the first RGB image is:

R=Y+1.403×(V-128);

G=Y-0.343×(U-128)-0.714×(V-128);

B=Y+1.77×(U-128).

In some embodiments of the present invention, optionally, in step 15, the conversion formula for converting the second RGB image to the second YUV image is:

Y=0.299×R+0.587×G+0.114×B;

U=-0.169×R-0.331×G+0.5×B+128;

V=0.5×R−0.419×G−0.081×B+128.

In the embodiment of the present invention, the first YUV image collected by the electronic digestive tract endoscope is converted into the first RGB image; the first RGB image is split, and the histogram equalization algorithm is used to separate the three single-color channel images Processing can avoid the interference of the existence of other color channels when processing one color channel image, thereby avoiding the problem of excessive contrast enhancement of perivascular tissue caused by direct processing of unsplit endoscopic images. It is beneficial to improve the pertinence of vascular imaging; in the embodiment of the present invention, after obtaining the processed three single-color channel images, merging the processed three single-color channel images is conducive to further improving the effect of image processing, so that the processed internal The endoscopic image has high contrast and high definition, which can meet the needs of users to efficiently identify blood vessels according to the endoscopic image, especially the identification of microvessels and venous vessels.

In some embodiments of the present invention, optionally, converting the first YUV image collected by the electronic digestive tract endoscope into a first RGB image includes:

Perform noise reduction processing on the first YUV image, where the noise reduction processing is used to reduce at least one of the following noises: luminance noise and color noise.

Image sensors may have problems with luminance noise and/or color noise due to a series of reasons such as their own properties (for example, cmos photosensitive elements are noisy), sensor manufacturing processes, and light entry conditions. In the embodiment of the present invention, before the first YUV image is converted into the first RGB image, noise reduction processing is performed on the first YUV image, which is beneficial to improve the image quality of the first RGB image after format conversion, and is beneficial to the subsequent The histogram equalization algorithm is used to improve the processing efficiency and processing effect in the process of processing three single-color channel images respectively.

The noise reduction processing methods for luminance noise and/or color noise are various and cannot be exhaustive; and the specific noise reduction processing method is not the protection point of the present invention, so it will not be repeated here. It can be understood that as long as the first YUV image with reduced luminance noise and/or color noise can be obtained, it should be considered to be included within the protection scope of the embodiments of the present invention.

In some embodiments of the present invention, optionally, a histogram equalization algorithm is used to process the three single-color channel images respectively to obtain three processed single-color channel images, including:

According to each of the single-color channel images, calculating the grayscale distribution data of each of the single-color channel images, and sending the grayscale distribution data to the interactive terminal associated with the user;

receiving a grayscale histogram limit value sent by the interactive terminal, where the limit value corresponds to the single-color channel image;

According to the limit value of the grayscale histogram, a histogram equalization algorithm is used to process the three single-color channel images respectively to obtain three processed single-color channel images;

or,

The three single-color channel images are respectively processed by the histogram equalization algorithm, and three single-color channel images after processing are obtained, including:

obtaining the preset limit value of the grayscale histogram;

According to the limit value of the grayscale histogram, a histogram equalization algorithm is used to process the three single-color channel images respectively to obtain three processed single-color channel images.

In the embodiment of the present invention, the grayscale distribution data is sent to the interactive terminal associated with the user, so that the user can set the grayscale histogram limit value with reference to the grayscale distribution data, and the user then sets the set grayscale histogram limit value It is sent to the execution body of the embodiment of the present invention through the interactive end. The execution body of the embodiment of the present invention receives the grayscale histogram limit value sent by the interactive terminal, and according to the grayscale histogram limit value, uses a histogram equalization algorithm to process the three single-color channel images respectively, Three single-color channel images are obtained after processing.

In the embodiment of the present invention, the grayscale histogram limit value is a preset value, and when the three single-color channel images are processed by the histogram equalization algorithm, the preset grayscale histogram limit value is obtained, According to the limit value of the grayscale histogram, a histogram equalization algorithm is used to process the three single-color channel images respectively to obtain three processed single-color channel images.

In some embodiments of the present invention, optionally, referring to FIG. 2 , FIG. 2 is the second schematic flowchart of a method for processing an electronic digestive tract endoscope image according to an embodiment of the present invention. The single-color channel image is processed to obtain three single-color channel images after processing, including:

Step 21: segment each single-color channel image according to a preset segmentation rule to obtain a plurality of image segments corresponding to the single-color channel image;

Step 22: Obtain the limit value of the grayscale histogram corresponding to the single-color channel image;

Step 23: Calculate the grayscale histogram of each image block of the single-color channel image, and calculate the grayscale of each image block of the single-color channel image according to the grayscale histogram limit value corresponding to the single-color channel image. The histogram is cropped to obtain the grayscale histogram of each image block after cropping;

Step 24: Obtain the grayscale mapping function of each sub-image according to the grayscale histogram of each image block after the cropping;

Step 25: for each image block, use an interpolation algorithm to interpolate the grayscale mapping function of the image block adjacent to the image block to obtain the target gray value of each image block;

Step 26: Enhance the contrast of the image blocks corresponding to the target gray value according to the target gray value to obtain the processed image blocks; merge the processed image blocks according to the preset segmentation rules to obtain the processed three image blocks. a single color channel image.

Exemplarily, each single-channel image is divided into grids (8×8 grid density, and each small grid is an image block); in addition, the three single-channel images are based on the same preset The segmentation rule is used for segmentation, so that the number of image blocks in the three single-channel images corresponds to their positions one-to-one.

In some embodiments of the present invention, optionally, the three single-channel images may be segmented according to the same preset segmentation rule, or the three single-channel images may not be segmented according to the same preset segmentation rule .

In some embodiments of the present invention, optionally, according to a specific preset segmentation rule, the image segments may be squares, rectangles, circles, and other geometric shapes.

In some embodiments of the present invention, optionally, according to a specific preset segmentation rule, the image blocks in the same single-channel image can be arranged in a regular and orderly manner, or they can be arranged in random order; each image block can also be It may or may not have the same area.

In the embodiment of the present invention, the formula of the grayscale mapping function is:

Among them: r is the gray level (0-255), H(r) is the histogram of the current gray level r, and N is the total number of pixels in the small block.

In some embodiments of the present invention, optionally, the interpolation algorithm used in step 25 may be a bilinear interpolation algorithm, that is, for each image block, the bilinear interpolation algorithm is used to analyze the adjacent images of the image blocks. The grayscale mapping function of the block is interpolated to obtain the target gray value of each image block.

Bilinear interpolation, also known as bilinear interpolation. Mathematically, bilinear interpolation is a linear interpolation extension of an interpolation function with two variables. The core idea is to perform a linear interpolation in two directions respectively. As an interpolation algorithm in numerical analysis, bilinear interpolation is widely used in signal processing, digital image and video processing and so on.

Referring to FIG. 3, FIG. 3 is a schematic diagram of the principle of the bilinear interpolation algorithm in the processing method of the electronic digestive tract endoscope image according to the embodiment of the present invention, and the corresponding formula of the bilinear interpolation algorithm is:

Among them, UL, UR, BL, and BR are the grayscale mapping functions of four small blocks around the pixel point, and the grayscale mapping functions of each small block are defined at its center position. d1, d2, d3, and d4 are the distances between the pixel and each grayscale mapping function. i is the original gray value of the pixel, and I is the new gray value of the pixel. For the original R, G, B, traverse all the pixels according to the above formula to obtain the R, G, B three-channel image with enhanced contrast.

In some embodiments of the present invention, optionally, referring to FIG. 4 , FIG. 4 is a third schematic flowchart of a method for processing an electronic digestive tract endoscope image according to an embodiment of the present invention. The grayscale histogram of each image block of the single-color channel image is cropped, and the grayscale histogram of each image block after cropping is obtained, including:

Step 31: Cropping step: Detecting each gray level in the grayscale histogram of the image block, for the gray level whose statistical value of the corresponding histogram exceeds the limit value of the grayscale histogram as the detection result, the corresponding gray level The statistical value of the histogram is cropped, so that the statistical value of the histogram after cropping does not exceed the limit value of the grayscale histogram;

Step 32: Evenly distribute the cropped histogram statistics to other gray levels whose detection result is that the corresponding histogram statistics value does not exceed the gray level histogram limit value; perform the cropping step until the gray level histogram of the image block. Each gray level in the figure does not exceed the limit value of the gray histogram, and the gray histogram of the cut image block is obtained.

Exemplarily, as shown in FIG. 5 , FIG. 5 is a schematic diagram of the principle of cropping the grayscale histogram of each image block of a single-color channel image. Set gray histogram limit values for R, G, and B images respectively: Climit_R, Climit_G, and Climit_B (the three values can be optimized according to the image). Each of the 64 small-block histograms in the R, G, and B images takes the corresponding limit values Climit_R, Climit_G, and Climit_B as the cropping amounts of their histograms. If a certain gray level in the small block histogram exceeds the limit value Climit, the histogram is cropped for the gray level, and the total number of cropped pixels for all gray levels in the small block is counted. The total amount obtained by clipping is equally divided into other uncropped gray levels of the histogram of the small block, and a new gray histogram of each small block is obtained. Repeat the above-mentioned cropping steps for each obtained new histogram of small blocks, until the new histogram of each small block does not exceed the corresponding limit value Climit, and each small block obtains the final new histogram (relative to the cutting in the embodiment of the present invention). grayscale histogram of the sliced image).

In some embodiments of the present invention, optionally, the histogram equalization algorithm is a contrast-limited adaptive histogram equalization CLAHE algorithm.

Ordinary adaptive histogram equalization AHE tends to amplify the contrast in near-constant regions of the image because the histograms in such regions are highly concentrated. As a result, AHE may cause noise to be amplified in a near-constant region. Contrast-limited AHE (CLAHE) is a variant of adaptive histogram equalization in which contrast amplification is limited, reducing this noise amplification problem.

In CLAHE, the contrast magnification around a given pixel value is given by the slope of the transform function. This is proportional to the slope of the neighborhood cumulative distribution function (CDF) and therefore to the value of the histogram at that pixel value. CLAHE limits upscaling by clipping the histogram to a predetermined value before computing the CDF. This limits the slope of the CDF and thus the slope of the transfer function. The value at which the histogram is clipped, the so-called clipping limit, depends on the normalization of the histogram and therefore on the size of the neighborhood. Common values limit result magnification to between 3 and 4. Advantageously, the part of the histogram that exceeds the clipping limit is not discarded, but it is distributed equally among all histogram blocks.

An embodiment of the present invention provides a device for processing an image of an electronic digestive tract endoscope. Referring to FIG. 6 , FIG. 6 is a schematic diagram of the principle of an image processing device of an electronic digestive tract endoscope according to an embodiment of the present invention. The processing device 70 includes:

The conversion module 71 is used to convert the first YUV image collected by the electronic digestive tract endoscope into a first RGB image;

A splitting module 72, configured to split the first RGB image to obtain three single-color channel images, where the three single-color channel images include: an R-channel image, a G-channel image, and a B-channel image;

The processing module 73 is configured to use the histogram equalization algorithm to process the three single-color channel images respectively to obtain three processed single-color channel images;

a merging module 74 for merging the processed three single-color channel images to obtain a second RGB image;

The conversion module 71 is further configured to convert the second RGB image into a second YUV image.

In some embodiments of the present invention, optionally, the apparatus 70 for processing an image of an electronic digestive tract endoscope further includes:

The noise reduction module 75 is configured to perform noise reduction processing on the first YUV image, and the noise reduction processing is used to reduce at least one of the following noises: luminance noise and color noise.

In some embodiments of the present invention, optionally,

The processing module 73 is further configured to calculate and obtain the grayscale distribution data of each of the single-color channel images according to each of the single-color channel images, and send the grayscale distribution data to the interactive terminal associated with the user;

The processing module 73 is further configured to receive a grayscale histogram limit value sent by the interactive terminal, where the limit value corresponds to the single-color channel image;

The processing module 73 is further configured to use a histogram equalization algorithm to process the three single-color channel images respectively according to the grayscale histogram limit value, to obtain three processed single-color channel images;

In some embodiments of the present invention, optionally,

The processing module 73 is further configured to obtain the preset limit value of the grayscale histogram;

The processing module 73 is further configured to process the three single-color channel images respectively by using a histogram equalization algorithm according to the grayscale histogram limit value, to obtain three processed single-color channel images.

In some embodiments of the present invention, optionally,

The processing module 73 is further configured to segment each of the single-color channel images according to preset segmentation rules to obtain multiple image segments corresponding to the single-color channel images;

The processing module 73 is further configured to obtain a grayscale histogram limit value corresponding to the single-color channel image;

The processing module 73 is further configured to calculate and obtain the grayscale histogram of each of the image blocks of the single-color channel image, and, according to the grayscale histogram limit value corresponding to the single-color channel image, for the single-color channel image. The grayscale histogram of each of the image blocks of the color channel image is trimmed to obtain the grayscale histogram of each of the image blocks after the cropping;

The processing module 73 is further configured to obtain a grayscale mapping function of each sub-image according to the cut grayscale histogram of each of the image blocks;

The processing module 73 is further configured to, for each image block, use an interpolation algorithm to interpolate the grayscale mapping function of the image blocks adjacent to the image block to obtain the target gray value of each image block;

The processing module 73 is further configured to enhance the contrast of the image blocks corresponding to the target gray value according to the target gray value, so as to obtain the processed image blocks; after merging and processing according to the preset segmentation rule The image is partitioned to obtain three single-color channel images after processing.

In some embodiments of the present invention, optionally,

The processing module 73 is also used for the cropping step: detecting each gray level in the grayscale histogram of the image block, and for the grayscale whose detection result is that the corresponding statistical value of the histogram exceeds the limit value of the grayscale histogram level, the histogram statistic value corresponding to the gray level is trimmed, so that the trimmed histogram statistic value does not exceed the gray histogram limit value;

The processing module 73 is further configured to evenly distribute the cropped histogram statistics to other gray levels whose detection results are that the corresponding histogram statistics do not exceed the grayscale histogram limit value; perform the cropping step Until each gray level in the grayscale histogram of the image block does not exceed the grayscale histogram limit value, the cut grayscale histogram of the image block is obtained.

The electronic digestive tract endoscope image processing apparatus provided in the embodiment of the present application can implement the various processes implemented by the method embodiments in FIGS. 1 to 5 , and achieve the same technical effect. To avoid repetition, details are not repeated here.

An embodiment of the present invention provides an electronic device 80. Referring to FIG. 7, FIG. 7 is a schematic block diagram of the electronic device 80 according to the embodiment of the present invention, including a processor 81, a memory 82, and a memory 82 and a processor 82. The program or instruction running on 81, when the program or instruction is executed by the processor, implements any one of the steps in the electronic digestive tract endoscope image processing method of the present invention.

An embodiment of the present invention provides a readable storage medium, on which a program or an instruction is stored, and when the program or instruction is executed by a processor, an embodiment of the processing method for an electronic digestive tract endoscope image as described above is implemented and can achieve the same technical effect, in order to avoid repetition, it will not be repeated here.

The readable storage medium is, for example, a read-only memory (Read-Only Memory, ROM for short), a random access memory (Random Access Memory, RAM for short), a magnetic disk, or an optical disk.

The embodiments of the present invention have been described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific embodiments, which are merely illustrative rather than restrictive. Under the inspiration of the present invention, without departing from the spirit of the present invention and the scope protected by the claims, many forms can be made, which all belong to the protection of the present invention.

Claims (10)

1. A method for processing an electronic digestive tract endoscope image, comprising:

converting a first YUV image acquired by an electronic digestive tract endoscope into a first RGB image;

splitting the first RGB image to obtain three single-color channel images, wherein the three single-color channel images comprise: an R channel image, a G channel image, and a B channel image;

processing the three single-color channel images by adopting a histogram equalization algorithm to obtain three processed single-color channel images;

merging the processed three single-color channel images to obtain a second RGB image;

and converting the second RGB image into a second YUV image.

2. The method for processing an electronic digestive tract endoscope image according to claim 1, characterized in that:

converting a first YUV image acquired by an electronic digestive tract endoscope into a first RGB image, comprising:

performing noise reduction processing on the first YUV image, wherein the noise reduction processing is used for reducing at least one of the following noises: luminance noise, color noise.

3. The method for processing an electronic digestive tract endoscope image according to claim 1, characterized in that:

processing the three single-color channel images by adopting a histogram equalization algorithm to obtain processed three single-color channel images, wherein the processing comprises the following steps:

calculating to obtain gray distribution data of each single-color channel image according to each single-color channel image, and sending the gray distribution data to an interaction terminal associated with a user;

receiving a gray level histogram limiting value sent by the interaction terminal, wherein the limiting value corresponds to the single-color channel image;

processing the three single-color channel images respectively by adopting a histogram equalization algorithm according to the gray level histogram limit value to obtain three processed single-color channel images;

or,

processing the three single-color channel images by adopting a histogram equalization algorithm to obtain processed three single-color channel images, wherein the processing comprises the following steps:

acquiring a preset gray level histogram limit value;

and processing the three single-color channel images respectively by adopting a histogram equalization algorithm according to the gray level histogram limit value to obtain the processed three single-color channel images.

4. The method for processing an electronic digestive tract endoscope image according to claim 1, characterized in that:

processing the three single-color channel images by adopting a histogram equalization algorithm to obtain processed three single-color channel images, wherein the processing comprises the following steps:

segmenting each single-color channel image according to a preset segmentation rule to obtain a plurality of image blocks corresponding to the single-color channel images;

acquiring a gray level histogram limit value corresponding to the single-color channel image;

calculating to obtain a gray histogram of each image block of the single-color channel image, and cutting the gray histogram of each image block of the single-color channel image according to a gray histogram limit value corresponding to the single-color channel image to obtain a cut gray histogram of each image block;

obtaining a gray mapping function of each sub-image according to the gray histogram of each cut image block;

for each image block, interpolating a gray mapping function of an adjacent image block of the image block by adopting an interpolation algorithm to obtain a target gray value of each image block;

enhancing the contrast of the image blocks corresponding to the target gray value according to the target gray value to obtain the processed image blocks; and combining the processed image blocks according to the preset segmentation rule to obtain the processed three single-color channel images.

5. The method for processing an electronic digestive tract endoscope image according to claim 4, characterized in that:

according to the gray histogram limit value corresponding to the single-color channel image, the gray histogram of each image block of the single-color channel image is cut to obtain the cut gray histogram of each image block, and the method comprises the following steps:

a cutting step: detecting each gray level in a gray level histogram of the image block, and cutting the histogram statistic value corresponding to the gray level aiming at the gray level of which the corresponding histogram statistic value exceeds the gray level histogram limit value as a detection result, so that the cut histogram statistic value does not exceed the gray level histogram limit value;

averagely distributing the cut histogram statistic values to other gray levels with the detection results that the corresponding histogram statistic values do not exceed the gray level histogram limit value; and executing the cutting step until all gray levels in the gray level histogram of the image block do not exceed the limit value of the gray level histogram, so as to obtain the cut gray level histogram of the image block.

6. The method of processing an electronic digestive tract endoscope image according to any one of claims 4 or 5, characterized in that:

the interpolation algorithm is a bilinear difference algorithm.

7. An apparatus for processing an electronic endoscope image of an alimentary tract, comprising:

the conversion module is used for converting a first YUV image acquired by the electronic digestive tract endoscope into a first RGB image;

a splitting module, configured to split the first RGB image to obtain three single-color channel images, where the three single-color channel images include: an R channel image, a G channel image, and a B channel image;

the processing module is used for respectively processing the three single-color channel images by adopting a histogram equalization algorithm to obtain three processed single-color channel images;

the merging module is used for merging the processed three single-color channel images to obtain a second RGB image;

the conversion module is further configured to convert the second RGB image into a second YUV image.

8. The apparatus for processing an electronic digestive tract endoscope image according to claim 7, characterized in that:

the processing module is further configured to segment each single-color channel image according to a preset segmentation rule to obtain a plurality of image blocks corresponding to the single-color channel image;

the processing module is further configured to obtain a gray level histogram limit value corresponding to the single-color channel image;

the processing module is further configured to calculate a gray histogram of each image partition of the single-color channel image, and cut the gray histogram of each image partition of the single-color channel image according to a gray histogram limit value corresponding to the single-color channel image to obtain a cut gray histogram of each image partition;

the processing module is further used for obtaining a gray mapping function of each sub-image according to the cut gray histogram of each image block;

the processing module is further used for interpolating the gray mapping functions of the image blocks adjacent to the image blocks by adopting an interpolation algorithm aiming at each image block to obtain a target gray value of each image block;

the processing module is further configured to enhance the contrast of the image partition corresponding to the target gray value according to the target gray value to obtain a processed image partition; and combining the processed image blocks according to the preset segmentation rule to obtain the processed three single-color channel images.

9. An electronic device, characterized in that: comprising a processor, a memory and a program or instructions stored on the memory and executable on the processor, which when executed by the processor implement the steps in the method of processing an electronic digestive tract endoscope image according to any one of claims 1 to 6.

10. A readable storage medium, characterized by: the readable storage medium stores thereon a program or instructions which, when executed by a processor, implement the steps in the method of processing an electronic digestive tract endoscope image according to any one of claims 1 to 6.

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