CN116993628A - CT image enhancement system for tumor radio frequency ablation guidance - Google Patents
CT image enhancement system for tumor radio frequency ablation guidance Download PDFInfo
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Abstract
The application relates to the field of image processing, in particular to a CT image enhancement system for tumor radio frequency ablation guidance, which comprises the following components: collecting a tumor radio frequency ablation CT image; obtaining a central boundary line in the neighborhood window according to the relation between each edge line in each central pixel point neighborhood window and the central pixel point; obtaining a tumor line suspected coefficient of each center boundary line according to the curvature radius vector of each center boundary line in each center pixel point neighborhood window and the corner information of two sides; obtaining a tumor emphasis coefficient of each central pixel point according to the suspected coefficient and the gradient difference of the tumor line of each central boundary line in each central pixel point neighborhood window; and according to the tumor enhancement coefficient of each edge point in the edge contour binary image and the gray level information of each pixel point in the tumor radio frequency ablation CT image, the gray level value of each pixel point after enhancement is obtained, and the intelligent enhancement of the tumor radio frequency ablation CT image is completed. The tumor pertinence is enhanced, and the algorithm accuracy is improved.
Description
Technical Field
The application relates to the field of image processing, in particular to a CT image enhancement system for tumor radio frequency ablation guidance.
Background
Along with the development of image technology, in order to realize accurate positioning puncture of tumors in a treatment body under CT guidance, compared with the traditional image positioning method, the spiral CT technology has the characteristics of higher scanning speed, clearer images and the like, and overcomes the defect that the position where a needle enters is difficult to determine due to instability of respiratory motion of the chest and abdomen, so that the spiral CT technology has important significance for treating tumors in the treatment body. The traditional local mean square error enhancement algorithm has poor processing effect on CT images, can not emphasize and enhance tumor areas in the images, and has no pertinence.
In summary, the application provides a CT image enhancement system for tumor radio frequency ablation guidance, which enhances a tumor radio frequency ablation CT image, performs tumor feature analysis on the tumor radio frequency ablation CT image based on a local mean square error enhancement algorithm by improving, and obtains the difference between a tumor and peripheral blood vessels, thereby extracting a tumor edge line, calculating an enhancement control coefficient of a central pixel point in each neighborhood window to influence an enhancement effect, avoiding the characteristic that the enhancement algorithm does not enhance a tumor region, and improving the accuracy of the enhancement effect.
Disclosure of Invention
In order to solve the technical problem, the application provides a CT image enhancement system for tumor radio frequency ablation guidance, which comprises:
and an image acquisition module: collecting a tumor radio frequency ablation CT image;
an image enhancement module: edge detection is carried out on the tumor radio frequency ablation CT image to obtain an edge contour binary image, and each edge point in the edge contour binary image is taken as a central pixel point to obtain a neighborhood window of each central pixel point;
obtaining a central boundary line in the neighborhood window according to the relation between each edge line in each central pixel point neighborhood window and the central pixel point;
extracting corner information on two sides of each central boundary line in each central pixel point neighborhood window by adopting a corner detection algorithm; obtaining a curvature radius vector sequence of each center boundary line according to the curvature of the boundary point on each center boundary line in each center pixel point neighborhood window;
obtaining a tumor line suspected coefficient of each central boundary line in each central pixel point neighborhood window according to the curvature radius vector sequence of each central boundary line in each central pixel point neighborhood window and the corner information on two sides of each central boundary line, and recording the central boundary line with the maximum tumor line suspected coefficient in each central pixel point neighborhood window as the final boundary line of each central pixel point neighborhood window;
calculating gray gradient rules at two sides of a final boundary line in a neighborhood window of each central pixel point;
obtaining the gradient difference of the final boundary line in the neighborhood window of the central pixel point according to the gray gradient rules at two sides of the final boundary line in the neighborhood window of each central pixel point;
obtaining a tumor emphasis coefficient of each center pixel point according to a tumor line suspected coefficient and a gradient difference of a final boundary line in a neighborhood window of each center pixel point;
and according to the tumor enhancement coefficient of each edge point in the edge contour binary image and the gray level information of each pixel point in the tumor radio frequency ablation CT image, the gray level value of each pixel point after enhancement is obtained, and the intelligent enhancement of the tumor radio frequency ablation CT image is completed.
Preferably, the specific step of obtaining the central boundary line in the neighborhood window according to the relation between each edge line in the neighborhood window of each central pixel point and the central pixel point is as follows:
and marking an edge line in the neighborhood window which passes through the central pixel point and intersects with the neighborhood window to form a closed area as a central boundary line in the neighborhood window according to the satisfaction of the neighborhood window of each central pixel point.
Preferably, the specific step of extracting the corner information on both sides of each center boundary line in each center pixel point neighborhood window by adopting a corner detection algorithm is as follows:
based on the each of the center pixel points,
respectively acquiring angular points at two sides of each central boundary line in the corresponding neighborhood window;
the corner number of the most side of the corner points in the two sides is marked as a first corner number, and the corner number of the least side of the corner points in the two sides is marked as a second corner number;
and (3) recording the ratio of the number of the first corner points to the number of the second corner points as corner point information on two sides of each central boundary line in each central pixel point neighborhood window.
Preferably, the specific step of obtaining the curvature radius vector sequence of each center boundary line according to the curvature of the boundary point on each center boundary line in each center pixel point neighborhood window is as follows:
calculating the curvature radius vector of each boundary point on each central boundary line of each central pixel point neighborhood window,
and according to the boundary points on the central boundary lines in the first neighborhood window from left to right and from top to bottom as initial starting points, arranging the boundary points on the central boundary lines in the neighborhood window into a sequence to obtain a curvature radius vector sequence of each central boundary line in the neighborhood window.
Preferably, the specific step of obtaining the tumor line suspected coefficient of each central boundary line in each central pixel point neighborhood window according to the curvature radius vector sequence of each central boundary line in each central pixel point neighborhood window and the corner point information on two sides of each central boundary line comprises the following steps:
based on the curvature radius vector sequence of each center boundary line in each center pixel point neighborhood window,
obtaining the curvature radius variance of each vector module length in the curvature radius vector sequence of each center boundary line, obtaining the curvature radius direction variance of each vector direction in the curvature radius vector sequence of each center boundary line,
the tumor line suspected coefficient of each central boundary line in each central pixel point neighborhood window is in direct proportion to the sum of the curvature radius variance and the curvature radius direction variance of each corresponding central boundary line, and in direct proportion to the corner point information on two sides of each corresponding central boundary line.
Preferably, the specific step of marking the central boundary line with the largest tumor line suspected coefficient in each central pixel point neighborhood window as the final boundary line of each central pixel point neighborhood window comprises the following steps:
obtaining the tumor line suspected coefficient of each central boundary line in each central pixel point neighborhood window,
and taking a central boundary line corresponding to the maximum value of the tumor line suspected coefficient in the neighborhood window as a final boundary line.
Preferably, the expression for calculating the gray level gradient law at two sides of the final boundary line in each central pixel point neighborhood window is as follows:
in the method, in the process of the application,for the number of boundary points on the final boundary line in the neighborhood window of the central pixel point, +.>Is the first +.o on the final boundary line in the neighborhood window of the central pixel point>Concave side area with boundary point as starting point and along boundary point +.>The number of pixels on a straight line along the radius of curvature direction of (a) is +.>As an exponential function based on natural constants, < +.>、/>Is the first +.o on the final boundary line in the neighborhood window of the central pixel point>Concave side area with boundary point as starting point and along boundary point +.>Is half of the curvature of (a)The +.f on the straight line where the radial direction is>Person, th->Gray values of two adjacent pixels, < >>And a gray level gradient rule of the concave side of the final boundary line of the neighborhood window of the central pixel point.
Preferably, the specific step of obtaining the gradient difference of the final boundary line in the neighborhood window of the central pixel point according to the gradient rule of the gray scales at two sides of the final boundary line in the neighborhood window of each central pixel point is as follows:
based on each central pixel point neighborhood window,
the side with the largest gray gradient rule among the two sides of the corresponding final boundary line is marked as a first gradient region,
the side with the smallest gray gradient rule of the two sides of the corresponding final boundary line is marked as a second gradient region,
and marking the ratio of the gray level gradient rule of the first gradient region to that of the second gradient region as the gradient difference of the final boundary line in the neighborhood window of the central pixel point.
Preferably, the specific steps of obtaining the tumor emphasis coefficient of each central pixel point according to the suspected coefficient and the gradient difference of the tumor line of the final boundary line in the neighborhood window of each central pixel point are as follows:
based on the final boundary line of each center pixel neighborhood window,
the gray average value corresponding to the areas at both sides of the final boundary is obtained,
the tumor emphasis coefficient of each central pixel point is in direct proportion to the suspected coefficient of the tumor line of the final boundary line in the neighborhood window of the central pixel point, the gradient difference and the absolute value of the difference value of the gray average values of the two side areas of the final boundary line.
Preferably, the expression of the gray value of each pixel point after the gray information of each pixel point in the tumor radio frequency ablation CT image is enhanced according to the tumor enhancement coefficient of each edge point in the edge contour binary image is:
in the method, in the process of the application,as a linear normalization function>For pixel point in the edge contour binary diagram, < ->For any pixel point in tumor radio frequency ablation CT image,>for the tumor emphasis coefficient benchmark value of each pixel point in the tumor radio frequency ablation CT image, the +.>The tumor emphasis coefficient for the center pixel in the pixel neighborhood window in the edge contour binary image,for in tumor radio frequency ablation CT image +.>Gray average value of pixel point in neighborhood window with the pixel point as central pixel point>Pixel points in radio frequency ablation CT image of tumor>Gray value of +.>Pixel points in radio frequency ablation CT image of tumor>Enhanced gray values.
The application has at least the following beneficial effects:
according to the application, the enhancement control coefficient in the local mean square error enhancement algorithm is corrected, the characteristic index is constructed according to the characteristics around the tumor edge points in the tumor radio frequency ablation CT image, the enhancement coefficient of each point in the image about the tumor edge points is evaluated, the characteristic that the enhancement algorithm does not enhance the tumor area is avoided, and the enhancement accuracy is improved.
According to the application, a neighborhood window with each edge point in the edge contour binary image of the tumor radio frequency ablation CT image as the center is analyzed, the difference characteristics between the tumor and surrounding blood vessels are extracted, and the edge line of the suspected tumor meeting the conditions in the neighborhood window is extracted by combining the corner point information, the gray level information and the curvature radius information of the pixel points, so that the preliminary positioning of the tumor edge line in the image is realized, and the interference of other conditions is eliminated; according to the method, the tumor line suspected coefficient of the center boundary line in the neighborhood window and the gray information of the two sides of the center boundary line are combined to obtain the tumor emphasized coefficient in the neighborhood window, the gray value of each pixel point in the tumor radio frequency ablation CT image after enhancement is obtained according to the tumor emphasized coefficient in the neighborhood window, and the suspected degree of the tumor edge line is accurately estimated; according to the application, the self-adaptive enhancement of the tumor radio-frequency ablation CT image is realized according to the enhanced image obtained by the tumor enhancement coefficient and gray information of each edge point in the image.
The application improves the pertinence of the enhancement range and has better enhancement effect.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the application, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a CT image enhancement system for tumor radio frequency ablation guidance provided by the present application.
Detailed Description
In order to further describe the technical means and effects adopted by the application to achieve the preset aim, the following is a detailed description of specific implementation, structure, characteristics and effects of a CT image enhancement system for tumor radio frequency ablation guidance according to the present application with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
The following specifically describes a specific scheme of a CT image enhancement system for tumor radio frequency ablation guidance provided by the present application with reference to the accompanying drawings.
The CT image enhancement system for tumor radio frequency ablation guidance provided by one embodiment of the application comprises an image acquisition module and an image enhancement module.
Specifically, the CT image enhancement system for tumor radio frequency ablation guidance of the present embodiment provides an intelligent enhancement guidance method for tumor radio frequency ablation CT, please refer to fig. 1, which includes the following steps:
and S001, an image acquisition module acquires a tumor radio frequency ablation CT image of the therapeutic body through spiral CT.
The embodiment mainly acquires a tumor radio frequency ablation CT image of a therapeutic body according to spiral CT, analyzes and extracts image information, and realizes treatment of suspected tumor edge lines according to distribution conditions of tumors and surrounding blood vessels in each edge point neighborhood window in the image. In this embodiment, a tumor radio frequency ablation CT image of a therapeutic body is acquired by spiral CT.
Because some noise inevitably occurs in the tumor radio frequency ablation CT image, the noise can cause a certain interference to the enhancement and positioning of the tumor region. In consideration of noise interference in the image acquisition process, the acquired tumor radio frequency ablation CT image is processed by adopting a median filtering technology, and the influence caused by noise and image motion interference is eliminated. It should be noted that the median filtering technique is a known technique, and may be implemented by the known technique. And are not described in detail herein to the extent that they are not within the scope of the present embodiments.
According to the method of the embodiment, the tumor image of the therapeutic body is acquired, and the tumor radio frequency ablation CT image is obtained.
Step S002, the image enhancement module analyzes the tumor characteristics in the neighborhood window with each edge point in the edge binary image as a central pixel point, extracts edge lines in the neighborhood window, analyzes the tumor edge point enhancement coefficient of each point in the image, and realizes the enhancement of the tumor radio frequency ablation CT image.
For the tumor radio frequency ablation CT image, the fact that the tumor area is not obvious in the tumor radio frequency ablation CT image and is interfered by other factors such as peripheral blood vessels is considered. Therefore, the embodiment sets the image enhancement module for performing feature analysis on each region in the tumor radio frequency ablation CT image, performs tumor feature extraction on the neighborhood window with each edge point in the edge binary image as the central pixel point, and calculates the tumor enhancement coefficient of each point in the tumor radio frequency ablation CT image, so as to realize accurate enhancement of the tumor region in the tumor radio frequency ablation CT image, and avoid the influence of lower enhancement effect of the tumor region caused by non-pertinence.
The image enhancement module in this embodiment mainly includes the following steps:
and detecting all edges in the tumor radio frequency ablation CT image by adopting a Canny operator to obtain an edge contour binary image. Because the tumor edge in the tumor radio frequency ablation CT image may be less clear, the tumor area in the image needs to be enhanced, so that the contrast of the image is displayed compared with the contrast of other areas, thereby helping doctors to more accurately identify the possibility of tumor diseases in the patient tumor radio frequency ablation CT image.
Because the edge of the tumor has irregular characteristics compared with blood vessels, and the tumor edge is positioned around the position along the radius of curvature of the edge, not only can a tumor area appear, but also some blood vessel areas exist, and the distribution of the blood vessels is more uniform, and the blood vessels have more blood vessel bifurcation relative to the tumor area.
For this case, the surroundings of each edge point in the edge contour binary image are calculatedEdge texture distribution within a neighborhood window. />Is set by the value-taking practitioner of (1), here is set to +.>. Calculating a curvature radius vector of each boundary point on a central boundary line passing through a central pixel point in a neighborhood window and forming a closed region with the neighborhood window>According to the first boundary point on the boundary line from left to right and from top to bottom as the initial starting point, obtaining the curvature radius vector sequence of each center boundary line in the neighborhood window +.>. And calculating the number of the corner points at two sides of each central boundary line in the neighborhood window by using a Harris corner detection algorithm, wherein if the number of the corner points at two sides is greatly different, the central boundary line in the neighborhood window can be a suspected edge line of a tumor. The tumor line suspected coefficient of each central boundary line in each central pixel point neighborhood window is obtained, and the expression is:
in the method, in the process of the application,for the number of corner points with the largest number in the two sides of the central boundary line in the neighborhood window of the central pixel point, +.>For the least number of corner points in the central boundary line in the central pixel neighborhood window, +.>A scaling factor representing the number of corner points on the most numerous side and the number of corner points on the least numerous side, the larger the value is, the more uneven the distribution of blood vessels on both sides of the line, wherein one side is more likely to be a tumor area; />For the variance of the curvature radius of each boundary point on the central boundary line in the neighborhood window of the central pixel point, the larger the value is, the more irregular the curvature radius distribution of the boundary point on the central boundary line in the neighborhood window is, namely the central boundary line in the neighborhood window is more curved compared with the blood vessel line; />For the variance of the curvature radius direction of each boundary point on the central boundary line in the neighborhood window of the central pixel point, the distribution of the central boundary line in the neighborhood window is more chaotic compared with the curvature radius direction of the blood vessel edge line; />Is the tumor line suspected coefficient of the central boundary line in the neighborhood window of the central pixel point,/and the center boundary line is the tumor line suspected coefficient of the central boundary line in the neighborhood window of the central pixel point>The larger the indicated central border line within the neighborhood window the more likely it is a tumor border line.
Repeating the method in the embodiment to obtain the suspected coefficient of the tumor line in the neighborhood window with each edge point in the edge contour binary image as the central pixel point; and taking a central boundary line with the maximum suspected coefficient of the tumor line in the neighborhood window of the central pixel point as a final boundary line.
The gray value of the tumor is changed when the edge is close to the central area of the tumor, and the change of the gray value is gradually increased; while the grey values at the vessel side do not show such a gradient law.
And aiming at the situation, calculating the change degree of the gradual change of the gray scale at each side of the final boundary line in the neighborhood window, and obtaining the gradual change rule of the gray scales at both sides of the final boundary line in the neighborhood window of the central pixel point. Taking the concave side of the final boundary line as an example, the gray scale gradient law of the side is calculated.
In the method, in the process of the application,for the number of boundary points on the final boundary line in the neighborhood window of the central pixel point, +.>Is the first +.o on the final boundary line in the neighborhood window of the central pixel point>Concave side area with boundary point as starting point and along boundary point +.>The number of pixels on a straight line along the radius of curvature direction of (a) is +.>As an exponential function based on natural constants, < +.>、/>Is the first +.o on the final boundary line in the neighborhood window of the central pixel point>Concave side area with boundary points as starting pointsAlong boundary points->Is the first +.on the straight line where the radius of curvature direction of (a) is located>Person, th->Gray values of two adjacent pixels are calculated by +.>The concave side area of each boundary point on the final boundary line is positioned on a straight line along the curvature radius direction>The gray scale change condition between adjacent pixel points is adjusted; the gray scale on the concave side of each boundary point on the final boundary line of the neighborhood window generally presents increasing variation by summing the values less than 0, which indicates that the side region may be a region where the gray scale value of the tumor edge decreases. />Gray scale gradient law of concave side of final boundary line of neighborhood window of central pixel point, +.>The smaller the gray scale change on the straight line of the concave side along the curvature radius direction of each boundary point on the final boundary line is, the more gradually, namely the side is possibly a tumor area.
And repeating the steps, so that the gray level gradient rule of the convex side of the final boundary line in the neighborhood window of the central pixel point can be obtained.
Since the tissue density of a tumor is generally greater than that of surrounding normal tissue, in a tumor radio frequency ablation CT image, the tumor is generally a high-density lesion area, and the brightness of the tumor area is greatly different from that of other surrounding areas.
For this case, the final boundary line within the neighborhood window is combinedThe gray average values at two sides of the final boundary line are calculated, and the larger the phase difference is, the more the gray distribution at two sides of the final boundary line is different, namely one of the two sides is possibly a tumor area. At the same time, the gray level gradient rule of the side with the maximum value of the two sides of the final boundary line is obtained>And the gray scale gradient law at the minimum sideThe gradient difference is used as a change characteristic that the gray gradient on one side of the tumor shows regular increase. And obtaining a tumor emphasis coefficient of each center pixel point according to the tumor line suspected coefficient of the final boundary line in the neighborhood window of each center pixel point and gray information on two sides of the final boundary line, wherein the specific expression is as follows:
in the method, in the process of the application,tumor line suspected coefficient for final boundary line in neighborhood window, < ->For the gray mean value on the side of the final border line in the neighborhood window, < >>For the gray mean value on the other side of the final boundary line in the neighborhood window, < >>In the method, by calculating the gray level difference at two sides, the larger the gray level difference at two sides of the edge line is, namely the possibility that tumors appear at one side of the edge line is higher; />For the side of the final boundary line of the neighborhood window of the central pixel point, where the value is the largestThe gradation of the gray is changed in a stepwise manner,the larger the ratio of the gradient difference is, the larger the gradient difference is, which indicates that the gradient difference is larger at the two sides of the final boundary line, and the larger the gradient difference is, which indicates that the side with the largest gradient rule is more likely to be a tumor region; />Tumor emphasis coefficient for central pixel neighborhood window, < ->The larger this represents the more similar the surrounding of the center pixel point to the tumor margin area, the more likely the center pixel point is to be a tumor margin point.
And according to the obtained tumor emphasis coefficient of each pixel point in the edge contour binary image, enhancing the tumor edge point in the tumor radio frequency ablation CT image according to the low-frequency and high-frequency components of each point in the image by changing the enhancement control coefficient in the local mean square error-based enhancement algorithm.
In the method, in the process of the application,as a linear normalization function>For pixel point in the edge contour binary diagram, < ->For any pixel point in tumor radio frequency ablation CT image,>for the tumor emphasis coefficient benchmark value of each pixel point in the tumor radio frequency ablation CT image, the +.>The tumor emphasis coefficient for the center pixel in the pixel neighborhood window in the edge contour binary image,for in tumor radio frequency ablation CT image +.>Gray average value of pixel point in neighborhood window with the pixel point as central pixel point>Pixel points in radio frequency ablation CT image of tumor>Gray value of +.>Pixel points in radio frequency ablation CT image of tumor>Enhanced gray values.
It should be noted that the number of the substrates,taking the +.f in the tumor radio frequency ablation CT image>The gray average value of the pixel point in the 5x5 neighborhood window with the pixel point as the central pixel point is used for determining +.>The pixel points at the positions are high-frequency or low-frequency components, so that local characteristics of the pixel points are enhanced to a corresponding degree.
By analyzing the situation around each edge point in the edge contour binary image, the algorithm emphasizes and enhances the tumor edge in the tumor radio frequency ablation CT image when the image is enhanced, so that the intelligent enhancement of the tumor radio frequency ablation CT image is completed.
In summary, in this embodiment, by correcting the enhancement control coefficient in the local mean square error enhancement algorithm, a feature index is constructed according to the features around the tumor edge points in the tumor radio frequency ablation CT image, and the enhancement coefficient of each point in the image with respect to the tumor edge points is evaluated, so that the feature that the enhancement algorithm does not enhance the tumor region is avoided, and the enhancement accuracy is improved.
According to the embodiment, the neighborhood window with each edge point in the edge contour binary image of the tumor radio frequency ablation CT image as the center is analyzed, the difference characteristics between the tumor and surrounding blood vessels are extracted, the corner point information, the gray level information and the curvature radius information of the pixel points are combined, the edge line of the suspected tumor meeting the conditions in the neighborhood window is extracted, the preliminary positioning of the tumor edge line in the image is realized, and the interference of other conditions is eliminated; according to the embodiment, the tumor line suspected coefficient of the center boundary line in the neighborhood window and the gray information of the two sides of the center boundary line are combined to obtain the tumor emphasized coefficient in the neighborhood window, the gray value of each pixel point in the tumor radio frequency ablation CT image after enhancement is obtained according to the tumor emphasized coefficient in the neighborhood window, and the suspected degree of the tumor edge line is accurately estimated; according to the embodiment, the self-adaptive enhancement of the tumor radio-frequency ablation CT image is realized according to the enhanced image obtained by the tumor enhancement coefficient and gray information of each edge point in the image.
The embodiment improves the pertinence of the enhancement range and has better enhancement effect.
It should be noted that: the sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
The foregoing description of the preferred embodiments of the present application is not intended to be limiting, but rather, any modifications, equivalents, improvements, etc. that fall within the principles of the present application are intended to be included within the scope of the present application.
Claims (10)
1. A CT image enhancement system for tumor radio frequency ablation guidance, the system comprising:
and an image acquisition module: collecting a tumor radio frequency ablation CT image;
an image enhancement module: edge detection is carried out on the tumor radio frequency ablation CT image to obtain an edge contour binary image, and each edge point in the edge contour binary image is taken as a central pixel point to obtain a neighborhood window of each central pixel point;
obtaining a central boundary line in the neighborhood window according to the relation between each edge line in each central pixel point neighborhood window and the central pixel point;
extracting corner information on two sides of each central boundary line in each central pixel point neighborhood window by adopting a corner detection algorithm; obtaining a curvature radius vector sequence of each center boundary line according to the curvature of the boundary point on each center boundary line in each center pixel point neighborhood window;
obtaining a tumor line suspected coefficient of each central boundary line in each central pixel point neighborhood window according to the curvature radius vector sequence of each central boundary line in each central pixel point neighborhood window and the corner information on two sides of each central boundary line, and recording the central boundary line with the maximum tumor line suspected coefficient in each central pixel point neighborhood window as the final boundary line of each central pixel point neighborhood window;
calculating the gray level gradient rule of the concave side and the convex side of the final boundary line in the neighborhood window of each central pixel point; obtaining the gradient difference of the final boundary line in the neighborhood window of the central pixel point according to the gray gradient rules at two sides of the final boundary line in the neighborhood window of each central pixel point;
obtaining a tumor emphasis coefficient of each center pixel point according to a tumor line suspected coefficient and a gradient difference of a final boundary line in a neighborhood window of each center pixel point;
and according to the tumor enhancement coefficient of each edge point in the edge contour binary image and the gray level information of each pixel point in the tumor radio frequency ablation CT image, the gray level value of each pixel point after enhancement is obtained, and the intelligent enhancement of the tumor radio frequency ablation CT image is completed.
2. The CT image enhancement system for tumor radio frequency ablation guidance according to claim 1, wherein the specific step of obtaining the center boundary line in the neighborhood window according to the relationship between each edge line in the neighborhood window of each center pixel point and the center pixel point is as follows:
and marking an edge line in the neighborhood window which passes through the central pixel point and intersects with the neighborhood window to form a closed area as a central boundary line in the neighborhood window according to the satisfaction of the neighborhood window of each central pixel point.
3. The CT image enhancement system for tumor radio frequency ablation guidance according to claim 1, wherein the specific step of extracting corner information on both sides of each center boundary line in each center pixel point neighborhood window by using a corner detection algorithm comprises the following steps:
based on the each of the center pixel points,
respectively acquiring angular points at two sides of each central boundary line in the corresponding neighborhood window;
the corner number of the most side of the corner points in the two sides is marked as a first corner number, and the corner number of the least side of the corner points in the two sides is marked as a second corner number;
and (3) recording the ratio of the number of the first corner points to the number of the second corner points as corner point information on two sides of each central boundary line in each central pixel point neighborhood window.
4. A CT image enhancement system for tumor radio frequency ablation guidance according to claim 1, wherein the specific step of obtaining the vector sequence of the radius of curvature vector of each center boundary line according to the curvature of each boundary point on each center boundary line in each center pixel point neighborhood window is as follows:
calculating the curvature radius vector of each boundary point on each central boundary line of each central pixel point neighborhood window,
and according to the boundary points on the central boundary lines in the first neighborhood window from left to right and from top to bottom as initial starting points, arranging the boundary points on the central boundary lines in the neighborhood window into a sequence to obtain a curvature radius vector sequence of each central boundary line in the neighborhood window.
5. The CT image enhancement system for tumor radio frequency ablation guidance according to claim 1, wherein the specific steps of obtaining the tumor line suspected coefficient of each center boundary line in each center pixel point neighborhood window according to the curvature radius vector sequence of each center boundary line in each center pixel point neighborhood window and the corner point information on both sides of each center boundary line are as follows:
based on the curvature radius vector sequence of each center boundary line in each center pixel point neighborhood window,
obtaining the curvature radius variance of each vector module length in the curvature radius vector sequence of each center boundary line, obtaining the curvature radius direction variance of each vector direction in the curvature radius vector sequence of each center boundary line,
the tumor line suspected coefficient of each central boundary line in each central pixel point neighborhood window is in direct proportion to the sum of the curvature radius variance and the curvature radius direction variance of each corresponding central boundary line, and in direct proportion to the corner point information on two sides of each corresponding central boundary line.
6. The CT image enhancement system for tumor radio frequency ablation guidance according to claim 1, wherein the specific step of marking the central boundary line with the largest tumor line suspected coefficient in each central pixel point neighborhood window as the final boundary line of each central pixel point neighborhood window is as follows:
obtaining the tumor line suspected coefficient of each central boundary line in each central pixel point neighborhood window,
and taking a central boundary line corresponding to the maximum value of the tumor line suspected coefficient in the neighborhood window as a final boundary line.
7. The CT image enhancement system for tumor radio frequency ablation guidance according to claim 1, wherein the expression for calculating the gray scale gradient law of the concave side of the final boundary line in the neighborhood window of each central pixel point is:
in the method, in the process of the application,for the number of boundary points on the final boundary line in the neighborhood window of the central pixel point, +.>Is the first +.o on the final boundary line in the neighborhood window of the central pixel point>Concave side area with boundary point as starting point and along boundary point +.>The number of pixels on a straight line along the radius of curvature direction of (a) is +.>As an exponential function based on natural constants, < +.>、/>Is the first +.o on the final boundary line in the neighborhood window of the central pixel point>Concave side area with boundary points as starting pointsAlong boundary points->Is the first +.on the straight line where the radius of curvature direction of (a) is located>Person, th->Gray values of two adjacent pixels, < >>And a gray level gradient rule of the concave side of the final boundary line of the neighborhood window of the central pixel point.
8. The CT image enhancement system for tumor radio frequency ablation guidance according to claim 1, wherein the specific step of obtaining the gradient difference of the final boundary line in the neighborhood window of the central pixel point according to the gradient rule of the final boundary line on both sides of the neighborhood window of each central pixel point comprises the following steps:
based on each central pixel point neighborhood window,
the side with the largest gray gradient rule among the two sides of the corresponding final boundary line is marked as a first gradient region,
the side with the smallest gray gradient rule of the two sides of the corresponding final boundary line is marked as a second gradient region,
and marking the ratio of the gray level gradient rule of the first gradient region to that of the second gradient region as the gradient difference of the final boundary line in the neighborhood window of the central pixel point.
9. The CT image enhancement system for tumor radio frequency ablation guidance according to claim 1, wherein the specific step of obtaining the tumor emphasis coefficient of each center pixel according to the tumor line suspected coefficient and the gradient difference of the final boundary line in the neighborhood window of each center pixel is as follows:
based on the final boundary line of each center pixel neighborhood window,
the gray average value corresponding to the areas at both sides of the final boundary is obtained,
the tumor emphasis coefficient of each central pixel point is in direct proportion to the suspected coefficient of the tumor line of the final boundary line in the neighborhood window of the central pixel point, the gradient difference and the absolute value of the difference value of the gray average values of the two side areas of the final boundary line.
10. The CT image enhancement system for tumor radio frequency ablation guidance according to claim 1, wherein the expression of the gray value of each pixel point after the enhancement according to the tumor enhancement coefficient of each edge point in the edge contour binary image and the gray information of each pixel point in the tumor radio frequency ablation CT image is:
in the method, in the process of the application,as a linear normalization function>For pixel point in the edge contour binary diagram, < ->For any pixel point in tumor radio frequency ablation CT image,>for the tumor emphasis coefficient benchmark value of each pixel point in the tumor radio frequency ablation CT image, the +.>Tumor emphasis coefficient for central pixel point in pixel point neighborhood window in edge contour binary image, < +.>For in tumor radio frequency ablation CT image +.>Gray average value of pixel point in neighborhood window with the pixel point as central pixel point>Pixel points in radio frequency ablation CT image of tumor>Gray value of +.>Pixel points in radio frequency ablation CT image of tumor>Enhanced gray values.
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