A method for processing angiographic images

Abstract

The present invention proposes a method for processing angiography images, which specifically includes the following steps: S1, obtaining a completely identical edge-extracted angiography image by copying the original angiography image; S2, processing the original angiography image by using an image smoothing algorithm to obtain an intermediate angiography image; S3, extracting an edge region in the edge-extracted angiography image; S4, mapping the edge region to the intermediate angiography image, determining the vascular region and the background region in the intermediate angiography image, improving the grayscale value of the vascular region, and increasing the contrast between the vascular region and the background region. In the present invention, denoising and edge region extraction are performed on two completely identical angiography images respectively, and the edge region is mapped to the denoising-processed image, so as to maintain edge information while ensuring the denoising effect.

Description

Technical Field

[0001] The invention relates to the technical field of image data processing, in particular to a method for processing angiography images. Background Art

[0002] Cardiovascular and cerebrovascular diseases, diabetes and tumors are the three major diseases that pose the greatest threat to humans. Among them, cardiovascular and cerebrovascular diseases are at the top of the list and are listed by the World Health Organization as one of the current overall work priorities for the entire organization. At present, the diagnosis of vascular diseases mainly relies on various angiography techniques. Angiography has become an important clinical diagnostic method and plays an important role in the diagnosis and treatment of head and neck and central nervous system diseases, heart and large vessel diseases, tumors and peripheral vascular diseases.

[0003] Due to the existence of complex conditions such as uneven distribution of contrast agents, attenuation, and uneven exposure of radiation, the image quality of angiography images is generally poor, and they need to be enhanced to be better used for subsequent diagnostic analysis. The existing processing methods introduce Gaussian filtering in the filtering process. Gaussian filtering does not consider the shape characteristics of the image and performs image smoothing, which will damage the edge information of the image and affect the recognition of blood vessels. Summary of the invention

[0004] In order to solve the technical problem that the processing of angiography images in the prior art may damage edge information and affect the identification of blood vessels, the method for processing angiography images proposed in the present invention specifically includes the following steps:

[0005] S1, obtaining an identical edge-extracted angiography image by copying the original angiography image;

[0006] S2, using an image smoothing algorithm to process the original angiography image to obtain an intermediate angiography image;

[0007] S3, extracting edge regions in edge extraction angiography images;

[0008] S4. Mapping the edge region to an intermediate state angiography image, determining the blood vessel region and the background region in the intermediate state angiography image, increasing the grayscale value of the blood vessel region, and increasing the contrast between the blood vessel region and the background region.

[0009] Furthermore, in S2, a two-dimensional Gaussian filter function is used to perform filtering to remove environmental noise in the image.

[0010] Furthermore, in S3, the extraction process specifically includes the following steps:

[0011] S31, calculating the background grayscale standard value and the blood vessel grayscale standard value;

[0012] S32, determining an average width of the blood vessels according to the blood vessel type;

[0013] S33, scanning the edge extraction angiography image according to the average width of the blood vessels to determine the blood vessel area;

[0014] S34, extracting edge feature pixel points in the blood vessel region;

[0015] S35, constructing a pixel matrix for each edge feature pixel point, judging and identifying the pixels other than the central pixel point in the pixel matrix one by one, and adding new edge feature pixel points;

[0016] S36. Based on the shape of the blood vessel region, connect each edge feature pixel point to form an edge region.

[0017] Furthermore, in said S31, the calculation process specifically includes the expert demarcating a partial background area without noise and a partial blood vessel area without doubt in the edge-extracted angiography image, calculating the grayscale mean of the partial background area as the background grayscale standard value, and calculating the grayscale mean of the partial blood vessel area as the blood vessel grayscale standard value.

[0018] Furthermore, in S33, the process of determining the blood vessel region includes:

[0019] S331, constructing a square scanning block with a side length twice the average width of the blood vessel, scanning the edges of the square scanning block from left to right and from top to bottom to extract the angiography image, judging whether the area is the first blood vessel area according to the gray value of the pixels in the square scanning block, and after the scanning is completed, combining all the first blood vessel areas together to form a provisional blood vessel area;

[0020] S332, construct a square scanning block with a side length equal to the average width of the blood vessel, scan the provisional blood vessel region from top to bottom with the square scanning block, determine whether the region is a second blood vessel region based on the grayscale value of the pixels in the square scanning block, and after the scanning is completed, combine all the second blood vessel regions together to form a final blood vessel region.

[0021] Furthermore, in S331, the method for determining the first blood vessel region is to calculate the regional average grayscale value of pixels in the square scanning block. If the regional average grayscale value is greater than (background grayscale standard value + blood vessel grayscale standard value) / 2, the corresponding region is marked as the first blood vessel region.

[0022] Furthermore, in S332, the method for determining the second blood vessel region is to calculate the regional average grayscale value of pixels in the square scanning block. If the regional average grayscale value is greater than (background grayscale standard value + blood vessel grayscale standard value) / 2, the corresponding region is marked as the second blood vessel region.

[0023] Further, in S34, each pixel is detected from the outside to the inside at the edge of the blood vessel area, the grayscale value of the adjacent pixel at the front of the pixel is G1, and the grayscale value of the adjacent pixel at the back of the pixel is G2. If |G1-background grayscale standard value|<first threshold, |G2-blood vessel grayscale standard value|<first threshold, or, |G1-blood vessel grayscale standard value|<first threshold, |G2-background grayscale standard value|<first threshold, then the pixel is an edge feature pixel.

[0024] Furthermore, in S35, the pixel matrix is ​​a 5X5 pixel matrix centered on the edge feature pixel point.

[0025] Furthermore, in S35, the specific method of judging and identifying is that when the pixel is not a known edge feature pixel and |the gray value of the pixel - the gray value of the center pixel| is less than the second threshold, the current pixel is identified as a new edge feature pixel.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] Denoising and edge region extraction were performed on two identical angiography images respectively, and the edge region was mapped to the denoised image, thereby maintaining the edge information while ensuring the denoising effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a flow chart of the method for processing angiography images of the present invention. DETAILED DESCRIPTION

[0029] The technical solution of the present invention is described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0030] like Figure 1 As shown, the method for processing angiography images proposed by the present invention specifically includes the following:

[0031] S1. Obtaining a completely identical edge-extracted angiography image by copying the original angiography image.

[0032] S2. Using an image smoothing algorithm to process the original angiography image to obtain an intermediate angiography image. Specifically, using a two-dimensional Gaussian filter function to filter and remove environmental noise in the image.

[0033] S3, extracting edge regions from edge extraction angiography images. The extraction process specifically includes the following steps:

[0034] S31, calculating the background grayscale standard value and the blood vessel grayscale standard value. The calculation process specifically includes the expert demarcating a part of the background area without noise and a part of the blood vessel area without doubt in the edge extraction angiography image, calculating the grayscale mean of the part of the background area as the background grayscale standard value, and calculating the grayscale mean of the part of the blood vessel area as the blood vessel grayscale standard value.

[0035] S32. Determine an average blood vessel width according to the blood vessel type.

[0036] S33, scanning the edge-extracted angiography image according to the average width of the blood vessels to determine the blood vessel area. The process of determining the blood vessel area includes:

[0037] S331, construct a square scanning block with a side length twice the average width of the blood vessel, scan the edges of the square scanning block from left to right and from top to bottom to extract the angiography image, and judge whether the area is the first blood vessel area according to the gray value of the pixels in the square scanning block. After the scanning is completed, all the first blood vessel areas are combined together to form a provisional blood vessel area. The method for judging the first blood vessel area is to calculate the regional average gray value of the pixels in the square scanning block. If the regional average gray value is greater than (background gray standard value + blood vessel gray standard value) / 2, the corresponding area is marked as the first blood vessel area.

[0038] S332, construct a square scanning block with a side length equal to the average width of the blood vessel, scan the provisional blood vessel region from top to bottom with the square scanning block, determine whether the region is the second blood vessel region according to the grayscale value of the pixels in the square scanning block, and after the scanning is completed, combine all the second blood vessel regions together to form the final blood vessel region. The method for determining the second blood vessel region is to calculate the regional average grayscale value of the pixels in the square scanning block, and if the regional average grayscale value is greater than (background grayscale standard value + blood vessel grayscale standard value) / 2, mark the corresponding region as the second blood vessel region.

[0039] S34, extract edge feature pixel points in the blood vessel region. Specifically, detect each pixel from the outside to the inside at the edge of the blood vessel region, the grayscale value of the front neighboring pixel of the pixel is G1, and the grayscale value of the rear neighboring pixel of the pixel is G2, if |G1-background grayscale standard value|<first threshold, |G2-blood vessel grayscale standard value|<first threshold, or, |G1-blood vessel grayscale standard value|<first threshold, |G2-background grayscale standard value|<first threshold, then the pixel is an edge feature pixel point.

[0040] S35, for each edge feature pixel point, a 5X5 pixel matrix centered on it is constructed, and the other pixels in the pixel matrix except the central pixel point are judged and identified one by one, and new edge feature pixels are added. The specific method of judging and identifying is that when the pixel is not a known edge feature pixel, and |the gray value of the pixel - the gray value of the central pixel point| < the second threshold, the current pixel is identified as a new edge feature pixel point.

[0041] S36. Based on the shape of the blood vessel region, connect each edge feature pixel point to form an edge region.

[0042] S4. Mapping the edge region to an intermediate state angiography image, determining the blood vessel region and the background region in the intermediate state angiography image, increasing the grayscale value of the blood vessel region, and increasing the contrast between the blood vessel region and the background region.

[0043] The above disclosure is only the preferred embodiment of the present invention, and certainly cannot be used to limit the scope of the present invention. It should be pointed out that for those skilled in the art, any equivalent changes made to the scheme of the present invention without departing from the design structure and principle of the present invention are considered to be within the protection scope of the present invention.

Claims

1. A method for processing angiographic images, characterized in that , the processing method specifically includes the following steps: S1, obtaining an identical edge-extracted angiography image by copying the original angiography image; S2, using an image smoothing algorithm to process the original angiography image to obtain an intermediate angiography image; S3, extracting edge regions in edge extraction angiography images; S4, mapping the edge region to an intermediate state angiography image, determining a blood vessel region and a background region in the intermediate state angiography image, increasing the grayscale value of the blood vessel region, and increasing the contrast between the blood vessel region and the background region; In S3, the extraction process specifically includes the following steps: S31, calculating the background grayscale standard value and the blood vessel grayscale standard value; S32, determining an average width of the blood vessels according to the blood vessel type; S33, scanning the edge extraction angiography image according to the average width of the blood vessels to determine the blood vessel area; S34, extracting edge feature pixel points in the blood vessel region; S35, constructing a pixel matrix for each edge feature pixel point, judging and identifying the pixels other than the central pixel point in the pixel matrix one by one, and adding new edge feature pixel points; S36. Based on the shape of the blood vessel region, connect each edge feature pixel point to form an edge region.

2. The processing method according to claim 1, characterized in that: In S2, a two-dimensional Gaussian filter function is used to perform filtering to remove environmental noise in the image.

3. The processing method according to claim 1, characterized in that: In S31, the calculation process specifically includes the expert demarcating a part of the background area and a part of the blood vessel area without noise in the edge extraction angiography image, calculating the grayscale mean of the part of the background area as the background grayscale standard value, and calculating the grayscale mean of the part of the blood vessel area as the blood vessel grayscale standard value.

4. The processing method according to claim 1, characterized in that: In S33, the process of determining the blood vessel region includes: S331, constructing a square scanning block with a side length twice the average width of the blood vessel, scanning the edges of the square scanning block from left to right and from top to bottom to extract the angiography image, judging whether the area is the first blood vessel area according to the gray value of the pixels in the square scanning block, and after the scanning is completed, combining all the first blood vessel areas together to form a provisional blood vessel area; S332, construct a square scanning block with a side length equal to the average width of the blood vessel, scan the provisional blood vessel region from top to bottom with the square scanning block, determine whether the region is a second blood vessel region based on the grayscale value of the pixels in the square scanning block, and after the scanning is completed, combine all the second blood vessel regions together to form a final blood vessel region.

5. The processing method according to claim 4, characterized in that: In S331, the method for determining the first blood vessel region is to calculate the regional average grayscale value of pixels in the square scanning block. If the regional average grayscale value is greater than (background grayscale standard value + blood vessel grayscale standard value) / 2, the corresponding region is marked as the first blood vessel region.

6. The processing method according to claim 4, characterized in that: In S332, the method for determining the second blood vessel region is to calculate the regional average gray value of pixels in the square scanning block. If the regional average gray value is greater than (background gray standard value + blood vessel gray standard value) / 2, the corresponding region is marked as the second blood vessel region.

7. The processing method according to claim 1, characterized in that: In the above S34, each pixel is detected from the outside to the inside at the edge of the blood vessel area, the grayscale value of the adjacent pixel at the front of the pixel is G1, and the grayscale value of the adjacent pixel at the back of the pixel is G2. If |G1-background grayscale standard value|<first threshold, |G2-blood vessel grayscale standard value|<first threshold, or, |G1-blood vessel grayscale standard value|<first threshold, |G2-background grayscale standard value|<first threshold, then the pixel is an edge feature pixel.

8. The processing method according to claim 1, characterized in that: In S35, the pixel matrix is ​​a 5X5 pixel matrix centered on the edge feature pixel point.

9. The processing method according to claim 1, characterized in that: In S35, the specific method of judging and identifying is that when the pixel point is not a known edge feature pixel point and |the gray value of the pixel point - the gray value of the center pixel point| is less than the second threshold, the current pixel point is identified as a new edge feature pixel point.

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