Canny model-based method for segmenting three-dimensional medical image

Abstract

The invention relates to a Canny model-based method for segmenting a three-dimensional medical image and belongs to the technical field of image processing. The method comprises the following steps of: capturing a three-dimensional section image Imnk comprising a user-interested target in an original three-dimensional medical image IMNK through user interaction; performing median filtering on the three-dimensional section image Imnk to remove image noise; acquiring a Canny edge information image Cmnk of the three-dimensional section image Imnk which is subjected to the median filtering by a Canny method; searching a target edge and extracting a complete enclosed target edge according to interaction point coordinates (i0, j0) near the target edge of a certain frame of image of the user; and extracting interested target edges Emnk of all frames in the three-dimensional section image Imnk, wherein the extracted interested target edges Emnk serve as a segmentation result of the three-dimensional medical image. The Canny model-based method for segmenting the three-dimensional medical image uses a few user interaction processes, has a small calculated amount and can rapidly and effectively extract edge information of interested targets in the three-dimensional medical image so as to complete the segmentation of the three-dimensional medical image.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to an interactive fast segmentation method of three-dimensional medical images. Background technique [0002] In order to accurately distinguish normal tissue structures and abnormal lesions in medical images, medical images need to be segmented. Traditional image segmentation methods mainly include: [0003] (1) Edge-based segmentation methods: usually use different properties between regions (such as gray discontinuity in the region) to divide the boundaries between regions, such methods include parallel differential operator methods (such as Roberts, Sobel, Laplacian, Marr and other operators), serial boundary search methods, methods based on surface fitting, etc.; [0004] (2) Region-based segmentation methods: Usually, the homogeneity within the same region is used to identify different regions in an image, including threshold methods, region growing and splitting and ...

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Problems solved by technology

[0005] However, due to the differences in the imaging principles of medical images and the characteristics of the tissues themselves, and the formation of images is subject to factors such as noise, field offset effects, local body effects, and tissue motion, between tissues, between tissues and organs, and between organs and organs. It is very necessary to quickly and accurately realize 3D medical image segmentation

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