Method for segmenting image with non-uniform gray scale based on level set function

Abstract

The invention provides a method for segmenting an image with non-uniform gray scale based on a level set function. The method comprises the following steps: constructing the level set function; constructing image segmentation energy functional; and carrying out level set function evolution so that the energy functional is minimized so as to obtain an image segmentation boundary, wherein, the energy functional comprises a global energy statistical item, a local energy statistical item and an energy penalty item. In the invention, on the basis of a C-V (Chan-Vese) model, a Gaussian kernel function is introduced and the local statistical information for the image with the non-uniform gray scale is fully utilized so as to optimize the 'energy' functional for a minimized closed curve; and by adding the energy penalty item, convergence of a signed distance function (SDF) is ensured and the time-consuming reinitialization process is avoided. Experiments prove that by adopting the method for segmenting the image with the non-uniform gray scale, clear and accurate segmentation results can be obtained and the segmentation accuracy and efficiency for the image with the non-uniform gray scale can be improved.

Description

technical field [0001] The invention relates to image segmentation, in particular to an image segmentation method with uneven gray levels based on a level set function. Background technique [0002] The Level Set Method (Level Set Method) is a novel method for solving the evolution of geometric curves. It expresses plane closed curves or three-dimensional closed surfaces in an implicit way, thus avoiding the tracking of the closed curve evolution process. Transform the curve evolution into a pure numerical solution problem of solving partial differential equations. When the level set method is used for the evolution of geometric curves, the curve is represented as a higher-dimensional parameter space, which avoids the parameterization process of the evolution curve, and any change in the topological structure of the curve will be automatically embedded in the numerical change of the level set function . It is easy to extend to high-dimensional situations under the support ...

AI Technical Summary

Problems solved by technology

[0003] In practical applications, due to the influence of factors such as uneven illumination, inherent defects of imaging equipment, and patient movement, the uneven distribution of image gray levels often occurs, especially in medical images.

The non-uniform image gray level brings severe challenges to the image segmentation problem and hinders the development of computer-aided diagnosis systems in the medical field.

In order to overcome the adverse effects of image grayscale inhomogeneity, foreign scholars have proposed a variety of solutions based on the idea of ​​correcting the offset field, which alleviated the impact of grayscale inhomogeneity on segmentation to a certain extent, but this method The anti-noise ability is weak, and the calculation is time-consuming, and the practical application value is not great

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