Region growth detection method based on multiple judgments

Abstract

The invention discloses a region growth detection method based on multiple judgments and belongs to the technical field of image processing. The region growth detection method is used for processing medical MRI (Magnetic Resonance Imaging) and CT (Computed Tomography) images and accurately extracting out the required tissue images. The region growth detection method provided by the invention comprises the steps: firstly, manually choosing an initial point of a target image to be detected, wherein the choosing range is a target range; then, gradually and forcefully expanding the growth range to all image data of the choosing range based on an original region growth scheme, optimizing matching of discrete points and detecting whether the discrete points belong to the growth region based on edge detection; continuously optimizing the growth results in a growth process through the initial point optimization standard gray to guarantee that the required large region images are matched and avoiding matching interference points which can meet the judgment conditions; finally, performing corrosion and expansion treatment on the growth result, removing the interference points, truly optimizing the whole matching process on the whole and obtaining the more accurate results.

Description

technical field [0001] The invention relates to computer mathematics, graphics and digital image processing technology, in particular to a region growth detection method based on multiple determinations. Background technique [0002] The Region Growing Algorithm (RGA) is a method that expands and grows the seed elements and finally obtains the segmented regions. It first specifies some elements (vertices, triangular faces or blocks, that is, a set of connected points and faces) on the original grid data according to the rules as seeds. Each seed corresponds to an initial state of the segmentation. Absorb the adjacent, unlabeled point elements that meet the growth conditions, and stop the growth process when no new point elements can be added to the segmentation set, or when the termination conditions are met, and then obtain the regional growth result. But the traditional region growing algorithm ignores the fact that in actual matching, the required part is a small proport...

AI Technical Summary

Problems solved by technology

However, the traditional region growing algorithm ignores the fact that in the actual matching, the required part is a small proportion of the two-dimensional image.

In addition, the traditional growth judgment condition only compares the relationship between the judgment points, but ignores the overall relationship between the two-dimensional image and the judgment point, which leads to matching more interference points and seriously affects the overall effect.

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