Mask image segmentation method and system

Abstract

The invention belongs to the technical field of computer image processing, and discloses a mask image segmentation method and system, and the method comprises the following steps: 1, mask image detection: extracting the contour, convex hulls and convex defects of a mask image and the four pieces of feature information of the convex defects; 2, convex defect screening: screening out convex defectsmeeting the following conditions: the distance between the farthest point and the contour is greater than a first threshold value, the distance between the starting point and the terminal point is greater than a second threshold value, and the distance between the farthest point and the contour is greater than 50% of the maximum value of the distances between the farthest points of all convex defects on the same convex hull and the contour; and 3, carrying out segmentation processing, namely, finding out the farthest points on the same convex hull from the screened convex defects, taking the farthest points as segmentation points, and carrying out connection line segmentation processing after pairwise sorting along the segmentation points. The invention also provides a mask image segmentation system. The system adopts the method to realize segmentation processing of the mask image. The problem of excessive or insufficient segmentation in mask image segmentation is solved.

Description

technical field [0001] The invention belongs to the technical field of computer image processing, and in particular relates to a mask image segmentation method and system, which can be used for the segmentation of cell mask images or other various mask images. Background technique [0002] Imaging technology and image processing technology are developing rapidly. People often need to process various types of images to extract the desired feature information in the image, and various image segmentation algorithms have emerged as the times require. [0003] When the elements that need to be extracted in the image do not overlap and cohere with each other, various image segmentation algorithms can perform image segmentation processing better. However, when the elements that need to be extracted in the image are dense or small in size, such as the case where similar objects are gathered together, typical examples include cell nucleus mask images and images of some working parts,...

AI Technical Summary

Problems solved by technology

However, when the elements that need to be extracted in the image are dense or small in size, such as the case where similar objects are gathered together, typical examples include cell nucleus mask images and images of some working parts, etc. Since the images obtained by the camera are two-dimensional, Therefore, there are often situations where two or more images of objects are glued together. Such glued images are very unfavorable for the extraction of image feature information, and it is difficult to distinguish them. It is prone to over-segmentation or under-segmentation of the image. In this case, even the simplest count of features in the image will be wrong

[0004] Existing various image segmentation algorithms cannot handle image segmentation with overlapping or cohesive elements well, and over-segmentation or under-segmentation are serious when processing barely, that is to say, these methods have more or less deficiencies. Therefore, it is necessary to improve the image segmentation method to achieve better image segmentation processing effect.

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