Multiple‐Channel Local Binary Fitting Model for Medical Image Segmentation. Issue 4 (1st October 2015)

• Record Type: Journal Article
• Authors: Guo, Qi; Wang, Long; Shen, Shuting
• Is Part Of: Chinese journal of electronics. Volume 24:Issue 4(2015)
• Page Start: 802
• Page End: 806
• Publication Date: 2015-10-01
• Subjects: Gaussian curve -- Penalty term -- Multiband active contour model -- Image segmentation; Gaussian processes -- image segmentation -- medical image processing -- numerical stability; numerical stability -- initialization curve -- Gaussian kernel function -- multilayer image segmentation -- multiband active contour model -- regional limit function -- innovative M‐L model -- medical image segmentation -- multiple‐channel local binary fitting model; Electronics -- Periodicals; Electronics -- China -- Periodicals; Electronics; China; Periodicals; 621.38105
• DOI: 10.1049/cje.2015.10.023
• Languages: English
• ISSNs: 1022-4653

Abstract

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.