Methods of Brain Extraction from Magnetic Resonance Images of Human Head: A Review

被引:0
|
作者
Praveenkumar S. [1 ]
Kalaiselvi T. [2 ]
Somasundaram K. [2 ]
机构
[1] Qualcomm Technologies Inc, San Diego, 92121, CA
[2] Department of Computer Science and Applications, Gandhigram Rural Institute, Tamil Nadu, Gandhigram
关键词
brain extraction methods; brain segmentation; CNN; deep learning methods; deform-able models; magnetic resonance image; MRI; skull stripping; U-Net; watershed algorithm;
D O I
10.1615/CritRevBiomedEng.2023047606
中图分类号
学科分类号
摘要
Medical images are providing vital information to aid physicians in diagnosing a disease afflicting the organ of a human body. Magnetic resonance imaging is an important imaging modality in capturing the soft tissues of the brain. Segmenting and extracting the brain is essential in studying the structure and pathological condition of brain. There are several methods that are developed for this purpose. Researchers in brain extraction or segmentation need to know the current status of the work that have been done. Such an information is also important for improving the existing method to get more accurate results or to reduce the complexity of the algorithm. In this paper we review the classical methods and convolutional neural network–based deep learning brain extraction methods. © 2023 by Begell House, Inc.
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页码:1 / 40
页数:39
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