Multiple‐Channel Local Binary Fitting Model for Medical Image Segmentation

Abstract

This study proposes an innovative M-L (Multiple-channel local binary fitting) model for medical image segmentation. Designed to improve upon existing image segmentation models, the M-L model introduces a regional limit function to the multi-band active contour model to enable multilayer image segmentation. The Gaussian kernel function is used to improve the previous model's robustness, necessitating the use of a new initialization curve which enhances the accuracy of segmentation results. Compared to existing image segmentation methods, the proposed M-L model improves numerical stability and efficiency through the introduction of a new penalty term and an increased step length. This simulation experiment verifies the advantages of the new M-L model for improved medical image segmentation, including increased efficiency and usability of the model.