Medical image segmentation method and system for fully-represented semi-supervised fast spectral clustering

Abstract

The invention discloses a medical image segmentation method and a medical image segmentation system for fully-represented semi-supervised fast spectral clustering. The method comprises the following steps: obtaining a to-be-treated medical image; circling in the medical image by a touch screen; extracting grey level of pixel, a spatial position and Gabor textural characteristics of the whole medical image, and carrying out characteristic normalization and characteristic dimension reduction treatment; carrying out All-In-One form representation onto reference information of a circled area; generating a graph-based relaxed clustering model based on a full-presenting semi-supervising mechanism; rearranging a quadratic term of the clustering model into a novel positive definite matrix; rewriting as a constraint type minimal enclosing ball form; estimating a final solution based on a rapid approximation strategy of the core set minimal enclosing ball; determining actual category number of clustering segmentation by a graph-based clustering indication vector; and dividing clustering indication components into different subsets based on a K means algorithm according to the category number. The system comprises an FPGA (Field Programmable Gate Array) module and an external device. The method and the system disclosed by the invention are simple to operate, good in real-time performance and high in accuracy.

Description

technical field [0001] The invention belongs to the technical field of intelligent medical image segmentation, and relates to a medical image segmentation method of full-expression semi-supervised fast spectrum clustering, and also relates to a medical image segmentation system for realizing the method. Background technique [0002] The rapid development of medical imaging technologies such as CT, MRI, and PET continues to promote the progress of modern medicine. Its role has been further developed from the non-invasive inspection and visualization of the anatomical structure of human tissues and organs to the use of disease diagnosis, treatment plan design and treatment effect. basic tool for evaluation. Medical clinical practice and research often need to measure the shape, boundary, cross-sectional area and volume of certain tissues and organs in the human body, so as to obtain important information about the pathology or function of the tissue. Here the medical image se...

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Problems solved by technology

[0008] The purpose of the present invention is to provide a medical image segmentation method with full representation and semi-supervised fast spectral clustering, which solves the problems in the prior art that cannot be compatible with multiple reference situations of medical image segmentation at the same time, and the fast processing capacity is not enough

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