Improved region growing method applied to coronary artery angiography image segmentation

Abstract

The invention relates to an improved region growing method which is applied to vessel segmentation and extraction in a coronary artery angiography image. The improved region growing method comprises the following steps of: preprocessing the image to obtain an original image capable of directly performing region growth; making a regulation and randomly generating a group of seed points; setting a stack data structure, enabling a newly grown pixel point to enter a stack, and taking out the point previously entering the stack to serve as a current point to be subjected to growth when the current point completes the growth; sequentially performing growth on each seed point, wherein a seed point gray value serves as an average value at a growing initial stage, and calculating a new average gray value when a new pixel point is grown every time along with the growth of the seed points; and completing the growth when no pixel point meeting growth standards exists and no seed point exists. The improved region growing method has the advantages that the seed points are automatically generated, no manual intervention is needed, the local average values around each pixel point serve as growth parameters in a growing process, the coronary artery angiography image with uneven brightness can be segmented, and the efficiency and the accuracy of the image segmentation are improved.

Description

1. Technical Field [0001] The present invention relates to image processing, in particular to an improved region growing method used for segmentation and extraction of blood vessels in coronary angiography images. 2. Background technology [0002] Medical image processing is an important branch in the field of image processing, and the segmentation and extraction of blood vessels is one of the key and difficult points of medical image processing. Coronary angiography is to insert a catheter after percutaneously piercing an artery, and inject a contrast agent, using X-rays to absorb the contrast agent and other human tissues to record the image of the blood vessel. Therefore, the image brightness is very uneven. Such as figure 1 Shown is a smooth filtered coronary angiogram, the brightness of the image is not uniform. [0003] The region growing algorithm was first proposed by Zucker. As a serial region algorithm, it starts from one or a set of starting seed points, and continuous...

AI Technical Summary

Problems solved by technology

[0006] 2. The growth criterion of traditional region growth is judged according to the similarity of adjacent pixels or the average pixel gray value. This criterion is difficult to segment images with uneven brightness

like figure 1 , the background at A is larger than the gray level of blood vessels at B, using similarity or global average gray level, it is easy to produce overgrowth or undergrowth

There are also some methods that use the statistical information of pixels as the growth criterion, but the amount of calculation of those methods is greatly increased.

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