Medical image segmentation method based on improved gradient vector flow model

Abstract

The invention discloses a medical image segmentation method based on an improved gradient vector flow model. The medical image segmentation method comprises the following steps: carrying out pretreatments on a fuzzy medical dermoscopy image, wherein the pretreatments include image equalization, wave filtration, edge detection and the like; firstly extracting a rough profile of the image based on a texture segmentation method, and acquiring valid information points of the image; replacing the calculation of a European space vector field with a method for calculating a gradient vector field in a Riemann space, thereby improving the calculation efficiency; and finally, carrying out fine treatment on the image by virtue of an improved gradient vector snake model, and extracting an accurate and smooth image profile, thereby providing reference to medical diagnosis. According to the medical image segmentation method, the rough profile of the dermoscopy image is extracted by virtue of the texture segmentation method, and the gradient vector flow model calculated by a gradient force field is improved, so that the profile of the fuzzy medical dermoscopy image can be accurately and rapidly extracted, and a segmentation result is displayed and provides a contrastive reference for the medical diagnosis.

Description

【Technical field】 [0001] The invention relates to the medical image segmentation field, in particular to a medical image segmentation method based on an improved GVF Snake model. 【Background technique】 [0002] With the increasingly prominent guiding role of medical imaging technology in clinical diagnosis and treatment, medical image segmentation technology has gradually become an important and applicable research field in the discipline of medical image research. The goal of medical image segmentation is to effectively distinguish the target object from the background image by extracting the features of the target object for the next step of medical analysis. Snake model has become one of the key technologies of image processing. In terms of medical image segmentation, the Snake model is a popular research topic. It has great advantages over traditional medical image segmentation methods, and provides reliable segmentation results for edge detection, image matching and 3D...

AI Technical Summary

Problems solved by technology

However, at present, these existing algorithms based on gradient vector flow (GVF Snake) model segmentation have two shortcomings: 1) When the image background structure is complex, there is no suitable method for determining the initial contour

2) The calculation complexity of the gradient vector field is high, and when the image pixels are large, there are limitations in processing and slow speed, etc.

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