Medical image segmentation method based on two-dimensional maximum entropy threshold C-V model

Abstract

The invention discloses a medical image segmentation method based on a two-dimensional maximum entropy threshold C-V model, and the method comprises the steps: firstly calculating a two-dimensional histogram of a point gray scale-region gray scale mean value of an input original image based on the gray scale map of the original image; secondly, determining an optimal threshold value by using a two-dimensional maximum entropy method, and pre-segmenting the image into a target part, a background part, a noise part and an edge part; secondly, defining an initial level set function according to apre-segmentation result, and defining the level set function as a piecewise constant function with only two function values 1 and -1; and finally, judging whether the pixel points of the noise and edge regions belong to a target or a background through an energy functional. According to the medical image segmentation method based on the two-dimensional maximum entropy threshold C-V model, the influence of noise and the position, shape and size of an initial contour on the effect and rate of curve evolution can be effectively solved, the energy functional is minimized through adoption of a point-by-point substitution test method, and the image segmentation speed is improved.

Description

technical field [0001] The invention relates to the technical field of digital image processing, in particular to a medical image segmentation method based on a two-dimensional maximum entropy threshold C-V model. Background technique [0002] The analysis and processing of medical images is an important application of digital image processing. It analyzes and processes various images collected by medical devices through computer software programs, and provides corresponding assistance for doctors to conduct accurate diagnosis and treatment of patients. Medical image analysis involves a variety of technologies, including image segmentation, 3D visualization, computer-aided remote diagnosis and treatment, and so on. Among them, image segmentation refers to distinguishing different regions with special meaning in the image, these regions do not cross each other, and each region satisfies the consistency of a specific region. The research on image segmentation of medical imagi...

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Problems solved by technology

[0005] The purpose of the present invention is to provide a medical image segmentation method based on the two-dimensional maximum entropy threshold C-V model, which can effectively solve the noise processing problem and the initial position influence curve evolution speed problem, and minimize the Energy functional, improved image segmentation speed

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