Residual-driven Fuzzy C-Means for Image Segmentation

版本 1.0.0 (5.1 MB) 作者: Cong Wang
We elaborate on residual-driven Fuzzy C-Means (FCM) for image segmentation published in IEEE/CAA JAS 2021 and IEEE TCYB 2023.
0.0
(0)
47.0 次下载
更新时间 2023/4/14

查看许可证

In this paper, we elaborate on residual-driven Fuzzy C-Means (FCM) for image segmentation, which is the first approach that realizes accurate residual (noise/outliers) estimation and enables noise-free image to participate in clustering. We propose a residual-driven FCM framework by integrating into FCM a residual-related regularization term derived from the distribution characteristic of different types of noise. Built on this framework, a weighted L2 -norm regularization term is presented by weighting mixed noise distribution, thus resulting in a universal residual-driven FCM algorithm in presence of mixed or unknown noise. Besides, with the constraint of spatial information, the residual estimation becomes more reliable than that only considering an observed image itself. Supporting experiments on synthetic, medical, and real-world images are conducted. The results demonstrate the superior effectiveness and efficiency of the proposed algorithm over its peers.
We also make a thorough comparative study of DSFCM and RFCM.

引用格式

Cong Wang (2024). Residual-driven Fuzzy C-Means for Image Segmentation (https://www.mathworks.com/matlabcentral/fileexchange/127758-residual-driven-fuzzy-c-means-for-image-segmentation), MATLAB Central File Exchange. 检索时间: .

MATLAB 版本兼容性
创建方式 R2023a
兼容任何版本
平台兼容性
Windows macOS Linux
标签 添加标签

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

版本 已发布 发行说明
1.0.0