Adaptive prior shape-based image segmentation method

Abstract

The invention relates to an adaptive prior shape-based image segmentation method in the technical field of image processing. The influence of noise interference on image segmentation is eliminated by an integer sign function; a constraint variation model is provided for the weight coefficient of a prior shape model and the conventional active contour model needs to be manually adjusted, so that the weight coefficient can be adaptively converged to a stable value; meanwhile, identification-based shape template selection is used for determining a certain prior shape template during segmentation, so that the problem that a prior shape model-based segmentation result cannot be obtained in the prior art is solved.

Description

technical field [0001] The invention relates to an image processing method, in particular to an adaptive image segmentation method based on a priori shape. Background technique [0002] Active contour models and level set methods have been widely used in image processing and machine vision. However, when the difference between the edge of the object and the background is not large, the evolution curve will leak, resulting in segmentation failure. Moreover, common segmentation models cannot extract meaningful objects if there is noise, clutter, or occlusion in the image. An effective approach is to incorporate some prior knowledge of the target into the framework of the active contour model. Low-level prior knowledge, such as information such as grayscale, color, texture, and motion, is often insufficient to represent target features, and the invariance of these features is usually not guaranteed in practical applications. In recent years, high-level knowledge, especially ...

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Problems solved by technology

[0006] Although the segmentation model based on prior shape can guarantee a certain segmentation accuracy when there is noise, clutter and occlusion in the background, the following problems still remain unsolved: Combining the prior shape model with the traditional active contour model , it is necessary to manually adjust the weight coefficient between these two terms, and there is no guideline to guide how to adjust the coefficient

If the coefficient is not adjusted properly, the segmentation results based on the prior shape model are often not obtained; most shape models use the signed distance function to describe the image, but because the space where the signed distance function is located is nonlinear, the statistical shape obtained by linear weighting The image described by the model and parametric shape template no longer satisfies the signed distance function

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