Open AccessMultiple‐Channel Local Binary Fitting Model for Medical Image Segmentation
Author(s) -
Guo Qi,
Wang Long,
Shen Shuting
Publication year - 2015
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2015.10.023
Subject(s) - image segmentation , initialization , scale space segmentation , segmentation , robustness (evolution) , computer science , artificial intelligence , segmentation based object categorization , computer vision , channel (broadcasting) , active contour model , image (mathematics) , pattern recognition (psychology) , computer network , biochemistry , chemistry , gene , programming language
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.