State-of-the-Art Methods for Brain Tissue Segmentation: A Review

被引：67
作者：
Dora L. [1 ]
Agrawal S. [2 ]
Panda R. [2 ]
Abraham A. [3 ]
机构：
[1] Department of Electrical and Electronics Engineering, Veer Surendra Sai University of Technology, Burla
[2] Department of Electronics and Computer Engineering, Veer Surendra Sai University of Technology, Burla
[3] Machine Intelligence Research Labs (MIR Labs), Auburn, 98071, WA
来源：
Panda, Rutuparna (r-ppanda@yahoo.co.in) | 2017年 / Institute of Electrical and Electronics Engineers Inc., United States卷 / 10期
关键词：
Brain tissue segmentation; clustering methods; feature extraction and classification-based methods; region-based methods; thresholding-based methods; validation measures;
D O I：
10.1109/RBME.2017.2715350
中图分类号：
学科分类号：
摘要：
Brain tissue segmentation is one of the most sought after research areas in medical image processing. It provides detailed quantitative brain analysis for accurate disease diagnosis, detection, and classification of abnormalities. It plays an essential role in discriminating healthy tissues from lesion tissues. Therefore, accurate disease diagnosis and treatment planning depend merely on the performance of the segmentation method used. In this review, we have studied the recent advances in brain tissue segmentation methods and their state-of-the-art in neuroscience research. The review also highlights the major challenges faced during tissue segmentation of the brain. An effective comparison is made among state-of-the-art brain tissue segmentation methods. Moreover, a study of some of the validation measures to evaluate different segmentation methods is also discussed. The brain tissue segmentation, content in terms of methodologies, and experiments presented in this review are encouraging enough to attract researchers working in this field. © 2017 IEEE.
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页码：235 / 249
页数：14
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