CN111340739A - Image processing method and system - Google Patents
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Abstract
Based on the method and the system provided by the invention, when the image in the human body is obtained through the endoscope, the image is subjected to image enhancement and contour detection processing, then the processed image is subjected to image feature comparison, and finally the similarity of each block is subjected to weight addition to obtain the final similarity, so that the in-vivo image can be restored more accurately through precise image processing, and the effect of reducing errors to the maximum extent is realized. Thereby improving the restoration degree of the images in the human body collected by the endoscope system.
Description
Technical Field
The present application relates to the field of image processing technologies, and in particular, to an image processing method and system.
Background
With the improvement of medical level, the demand of medical devices in the medical community is more and more, and doctors must go through more efficient and deep medical examination and inspection devices to better determine the actual state of illness of patients and then make the most reasonable treatment.
The endoscope is designed for doctors to more accurately observe ulcers or tumors in the stomach of patients, and simultaneously transmits images in the stomach for the doctors to judge. The size of the endoscope and the accuracy of the image are very important because the endoscope is introduced into the body through the mouth of the patient.
Since the environment in the body is unstable, the algorithm of image processing is quite important.
However, the complexity of the current image processing method is high and the degree of restoration is also low.
Disclosure of Invention
The invention provides an image processing method and system, which are used for solving the problems of high complexity and low restoration degree of an image processing method in an endoscope in the prior art.
The specific technical scheme is as follows:
a method of image processing, the method comprising:
when an image in a human body is obtained, carrying out Gaussian filtering processing on the image to obtain a first image after filtering processing;
carrying out image enhancement processing on the first image to obtain a second image after the image enhancement processing;
determining gradient values of all points in the second image, and extracting an image contour in the second image according to the gradient values;
obtaining image characteristics of the image according to the image contour, comparing the image characteristics with the diseased image, and obtaining a comparison result;
and outputting the comparison result on a display device.
Optionally, performing image enhancement processing on the first image to obtain a second image after the image enhancement processing, including:
determining the variation value of the neighborhood intensity of each point in the first image;
obtaining enhanced image pixel points according to the change values of the input pixel points and a specified formula;
and obtaining a second image based on the enhanced image pixels.
Optionally, the determining the gradient value of each point in the second image, and extracting the image contour in the second image according to the gradient value includes:
calculating the gradient amplitude value of each pixel point according to the determined formula;
obtaining an image gradient amplitude value matrix according to the gradient amplitude value of each pixel point;
and determining the image contour in the second image according to the value of the image gradient amplitude value matrix.
Optionally, obtaining an image feature of the image according to the image contour, comparing the image feature with the diseased image, and obtaining a comparison result, including:
carrying out segmentation processing on the image contour, and carrying out matching similarity calculation on each image obtained after the segmentation processing to obtain a similarity vector;
calculating the distance of the similarity vector according to the obtained similarity vector;
and obtaining a comparison result of the image features and the diseased image based on the similarity vector distance.
An image processing system, the system comprising:
the filtering processing module is used for carrying out Gaussian filtering processing on the image when the image in the human body is obtained to obtain a first image after filtering processing;
the image enhancement processing module is used for carrying out image enhancement processing on the first image to obtain a second image after the image enhancement processing;
the contour detection module is used for determining gradient values of all points in the second image and extracting an image contour in the second image according to the gradient values;
the image comparison module is used for obtaining the image characteristics of the image according to the image contour, comparing the image characteristics with the diseased image and obtaining a comparison result;
and the output module is used for outputting the comparison result on the display equipment.
Optionally, the image enhancement processing module is specifically configured to determine a variation value of the intensity of each point neighborhood in the first image; obtaining enhanced image pixel points according to the change values of the input pixel points and a specified formula; and obtaining a second image based on the enhanced image pixels.
Optionally, the contour detection module is specifically configured to calculate a gradient amplitude value of each pixel according to a determined formula; obtaining an image gradient amplitude value matrix according to the gradient amplitude value of each pixel point; and determining the image contour in the second image according to the value of the image gradient amplitude value matrix.
Optionally, the image comparison module is specifically configured to segment the image contour, and perform matching similarity calculation on each image obtained after the segmentation to obtain a similarity vector; calculating the distance of the similarity vector according to the obtained similarity vector; and obtaining a comparison result of the image features and the diseased image based on the similarity vector distance.
Based on the method provided by the invention, when the image in the human body is obtained through the endoscope, the image is subjected to image enhancement and contour detection processing, then the processed image is subjected to image characteristic comparison, and finally the similarity of each block is subjected to weight addition to obtain the final similarity, so that the effect of restoring the image in the human body more accurately through precise image processing and reducing errors to the maximum extent is realized. Thereby improving the restoration degree of the images in the human body collected by the endoscope system.
Drawings
FIG. 1 is a flowchart of an image processing method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an image processing system according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention are described in detail with reference to the drawings and the specific embodiments, and it should be understood that the embodiments and the specific technical features in the embodiments of the present invention are merely illustrative of the technical solutions of the present invention, and are not restrictive, and the embodiments and the specific technical features in the embodiments of the present invention may be combined with each other without conflict.
Fig. 1 is a flowchart of an image processing method according to an embodiment of the present invention, where the method includes:
s1, when the image in the human body is obtained, Gaussian filtering processing is carried out on the image to obtain a first image after filtering processing;
s2, performing image enhancement processing on the first image to obtain a second image after the image enhancement processing;
s3, determining the gradient value of each point in the second image, and extracting the image contour in the second image according to the gradient value;
s4, obtaining image characteristics of the image according to the image contour, comparing the image characteristics with the diseased image, and obtaining a comparison result;
and S5, outputting the comparison result on a display device.
First, the method is applied to a system, which can be an endoscope system, and the system comprises five parts:
1. a camera portion responsible for acquiring images;
2. a central processing part responsible for image processing;
3. key part responsible for processing keys for man-machine interaction
4. A motor control part responsible for air exchange and supply;
5. and a display part responsible for displaying the result.
Based on the endoscope system, after the images are collected by the camera part, the images collected in the human body are transmitted to the central processing unit by the camera, and the central processing unit compares the images in the human body with the images of actual illness and analyzes the current specific diseases of the human body. The method specifically comprises the following steps: gaussian filtering processing, image enhancement, contour detection and image comparison.
Firstly, Gaussian filtering processing:
specifically, first, a gaussian filtering process is performed on a human body image to obtain a first image, and the gaussian filtering process is specifically implemented as follows:
a set of normalized Gaussian kernels is generated by using a discrete Gaussian filter function, and then each point of the image gray matrix is convolved based on the Gaussian kernel function. Convolving the image gray level I with a Gaussian kernel:
Iσ(x)＝I(x)*G(x)
the Gaussian kernel function is:
where the size of the gaussian kernel.
Secondly, image enhancement processing:
after the image filtering process is completed, image enhancement needs to be performed, and the image enhancement is specifically realized as follows:
determining the change value of the neighborhood intensity of each point in the first image, obtaining enhanced image pixel points according to the change value of the input pixel points and a specified formula, and obtaining a second image based on the enhanced image pixels.
Specifically, the basis of image enhancement is to determine the variation value of the intensity of each point neighborhood of the image. The enhancement algorithm can highlight points with significant changes in intensity values in the neighborhood of gray points of the image.
In the invention, the first image is enhanced by a point operation mode, namely, an enhanced image pixel is obtained by multiplying an input pixel value by a constant a and adding a constant b:
g(x)＝a*f(x)+b
where g (x) is the enhanced image pixel and f (x) is the input pixel.
Thirdly, the method comprises the following steps: contour detection processing:
after the image enhancement is completed, the contour detection of the second image is performed, that is, the gradient amplitude value of each pixel point is calculated according to the determined formula, an image gradient amplitude value matrix is obtained according to the gradient amplitude value of each pixel point, and the image contour in the second image is determined according to the value of the image gradient amplitude value matrix.
Specifically, the Canny edge detection operator is used in the present invention to identify the contours of the image.
The convolution operator adopted in the Canny operator in the algorithm is as follows:
the above formula is a template for calculating partial derivatives of the image in x and y directions, and the gradient amplitude of each point can be expressed by a mathematical formula as follows:
G[i，j]＝|f[i，j]-f[i+1，j+1]|+|f[i+1，j]-f[i，j-1]|
the mathematical expressions of the first-order partial derivative matrix, the gradient amplitude and the gradient direction in the x direction and the y direction are as follows:
P[i，j]＝(f[i，j+1]-f[i，j]+f[i+1，j+1]-f[i+1，j])/2
Q[i，j]＝(f[i，j]-f[i+1，j]+f[i，j+1]-f[i+1，j+1])/2
θ[i，j]＝arctan(Q[i，j]/P[i，j])
the larger the value of an element in the image gradient magnitude matrix M [ i, j ], the larger the gradient value of the point in the image, i.e. the point is an edge, i.e. a contour portion of the image.
Fourthly, image contrast processing:
after the outline of the image is obtained, the main characteristics of the image can be obtained, and the characteristics can be compared with the diseased image to judge whether the disease is diseased.
In the technology, a block comparison method is adopted to compare two images, and then a result is obtained. The image is cut into a plurality of blocks, similarity is calculated by matching each block respectively to obtain a similarity vector, and the vector distance is calculated to obtain the similarity of the two images.
Comparing each block in a pixel-by-pixel comparison mode, if the interpolation value is within a set range, the similarity is +1, and finally the similarity of the cutting block can be obtained by dividing the number of the obtained similar points by the total number of the points.
The following algorithm is used for the calculation of the distance between the vectors:
wherein d represents a vector distance, and x and y represent pixel points.
The inter-vector distance is used to determine the error range.
And finally, weighting and adding the similarity of each block to obtain the final similarity. If the similarity is greater than 90%, the suspected disease can be judged.
Based on the method provided by the invention, when the image in the human body is obtained through the endoscope, the image is subjected to image enhancement and contour detection processing, then the processed image is subjected to image characteristic comparison, and finally the similarity of each block is subjected to weight addition to obtain the final similarity, so that the effect of restoring the image in the human body more accurately through precise image processing and reducing errors to the maximum extent is realized. Thereby improving the restoration degree of the images in the human body collected by the endoscope system.
Corresponding to the method provided by the present invention, an embodiment of the present invention further provides an image processing system, and as shown in fig. 2, the image processing system in the embodiment of the present invention is schematically configured, and the system includes:
the filtering processing module 201 is configured to, when an image in a human body is obtained, perform gaussian filtering processing on the image to obtain a first image after filtering processing;
the image enhancement processing module 202 is configured to perform image enhancement processing on the first image to obtain a second image after the image enhancement processing;
the contour detection module 203 is configured to determine gradient values of each point in the second image, and extract an image contour in the second image according to the gradient values;
the image comparison module 204 is configured to obtain an image feature of the image according to the image contour, compare the image feature with the diseased image, and obtain a comparison result;
an output module 205, configured to output the comparison result on a display device.
Further, in this embodiment of the present invention, the image enhancement processing module 202 is specifically configured to determine a variation value of the intensity of each point in the first image in the neighborhood; obtaining enhanced image pixel points according to the change values of the input pixel points and a specified formula; and obtaining a second image based on the enhanced image pixels.
Further, in the embodiment of the present invention, the contour detection module 203 is specifically configured to calculate a gradient amplitude value of each pixel according to a determined formula; obtaining an image gradient amplitude value matrix according to the gradient amplitude value of each pixel point; and determining the image contour in the second image according to the value of the image gradient amplitude value matrix.
Further, in the embodiment of the present invention, the image comparison module 204 is specifically configured to perform segmentation processing on an image contour, and perform matching similarity calculation on each image obtained after the segmentation processing to obtain a similarity vector; calculating the distance of the similarity vector according to the obtained similarity vector; and obtaining a comparison result of the image features and the diseased image based on the similarity vector distance.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following appended claims be interpreted as including the preferred embodiment and all such alterations and modifications as fall within the scope of the application, including the use of specific symbols, labels, or other designations to identify the vertices.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (8)
1. An image processing method, characterized in that the method comprises:
when an image in a human body is obtained, carrying out Gaussian filtering processing on the image to obtain a first image after filtering processing;
carrying out image enhancement processing on the first image to obtain a second image after the image enhancement processing;
determining gradient values of all points in the second image, and extracting an image contour in the second image according to the gradient values;
obtaining image characteristics of the image according to the image contour, comparing the image characteristics with the diseased image, and obtaining a comparison result;
and outputting the comparison result on a display device.
2. The method of claim 1, wherein performing image enhancement processing on the first image to obtain an image-enhanced second image comprises:
determining the variation value of the neighborhood intensity of each point in the first image;
obtaining enhanced image pixel points according to the change values of the input pixel points and a specified formula;
and obtaining a second image based on the enhanced image pixels.
3. The method of claim 1, wherein determining gradient values for points in the second image and extracting image contours in the second image based on the gradient values comprises:
calculating the gradient amplitude value of each pixel point according to the determined formula;
obtaining an image gradient amplitude value matrix according to the gradient amplitude value of each pixel point;
and determining the image contour in the second image according to the value of the image gradient amplitude value matrix.
4. The method of claim 1, wherein obtaining image features of an image based on the image contour and comparing the image features to a pathology image and obtaining a comparison result comprises:
carrying out segmentation processing on the image contour, and carrying out matching similarity calculation on each image obtained after the segmentation processing to obtain a similarity vector;
calculating the distance of the similarity vector according to the obtained similarity vector;
and obtaining a comparison result of the image features and the diseased image based on the similarity vector distance.
5. An image processing system, characterized in that the system comprises:
the filtering processing module is used for carrying out Gaussian filtering processing on the image when the image in the human body is obtained to obtain a first image after filtering processing;
the image enhancement processing module is used for carrying out image enhancement processing on the first image to obtain a second image after the image enhancement processing;
the contour detection module is used for determining gradient values of all points in the second image and extracting an image contour in the second image according to the gradient values;
the image comparison module is used for obtaining the image characteristics of the image according to the image contour, comparing the image characteristics with the diseased image and obtaining a comparison result;
and the output module is used for outputting the comparison result on the display equipment.
6. The system according to claim 5, wherein the image enhancement processing module is specifically configured to determine a variation value of intensity in a neighborhood of points in the first image; obtaining enhanced image pixel points according to the change values of the input pixel points and a specified formula; and obtaining a second image based on the enhanced image pixels.
7. The system of claim 5, wherein the contour detection module is specifically configured to calculate a gradient magnitude value for each pixel point according to a determined formula; obtaining an image gradient amplitude value matrix according to the gradient amplitude value of each pixel point; and determining the image contour in the second image according to the value of the image gradient amplitude value matrix.
8. The system of claim 5, wherein the image comparison module is specifically configured to segment an image contour and perform matching similarity calculation on each image obtained after the segmentation to obtain a similarity vector; calculating the distance of the similarity vector according to the obtained similarity vector; and obtaining a comparison result of the image features and the diseased image based on the similarity vector distance.
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| WO2022016396A1 (en) * | 2020-07-21 | 2022-01-27 | 深圳先进技术研究院 | Medical image processing method and processing apparatus, and computer readable storage medium |
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Application publication date: 20200626 |