Rough Spatial Ensemble Kernelized Fuzzy C Means Clustering for Robust Brain MR Image Tissue Segmentation

被引:0
|
作者
Halder, Amiya [1 ]
Choudhuri, Rudrajit [1 ]
Bhowmick, Arinjay [1 ]
机构
[1] St Thomas Coll Engn & Technol, 4 DH Rd, Kolkata, India
来源
COMPUTER VISION AND IMAGE PROCESSING, CVIP 2023, PT III | 2024年 / 2011卷
关键词
Iterative Optimization; Magnetic Resonance Imaging; Image Segmentation; Rough Set; Kernel Method; ALGORITHM; INFORMATION;
D O I
10.1007/978-3-031-58535-7_29
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Image segmentation is a crucial step in image processing having various applications in biomedical image analysis. Segmentation of the magnetic resonance images of the brain is one such key area in biomedical image analysis that segments various tissues in the brain and detects tumor regions. In this paper, an unsupervised rough spatial ensemble kernelized fuzzy clustering segmentation algorithm is presented for automated segmentation of magnetic resonance images of the brain. The proposed algorithm is an integration of Rough Fuzzy C Means clustering and the kernel method with a novel ensemble kernel being a combination of spherical kernel, Gaussian, and Cauchy kernels, which improves the performance of the segmentation algorithm. The proposed algorithm performs better than the existing clustering algorithms across a wide range of magnetic resonance images of the brain along with visual indications obtained from the results.
引用
收藏
页码:350 / 363
页数:14
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