Segmentation and clustering in brain MRI imaging

被引：51
|
作者：
Mirzaei, Golrokh [4 ]
Adeli, Hojjat [1 ,2 ,3 ]
机构：
[1] Ohio State Univ, Dept Biomed Informat, Columbus, OH 43210 USA
[2] Ohio State Univ, Dept Neurol, Columbus, OH 43210 USA
[3] Ohio State Univ, Dept Neurosci, Columbus, OH 43210 USA
[4] Ohio State Univ, Dept Comp Sci & Engn, Marion, OH 43302 USA
来源：
REVIEWS IN THE NEUROSCIENCES | 2019年 / 30卷 / 01期
关键词：
clustering; convolutional neural network; FCM; K-means; segmentation; CONVOLUTIONAL NEURAL-NETWORKS; ANT COLONY OPTIMIZATION; DEEP LEARNING-MODEL; C-MEANS ALGORITHM; TUMOR SEGMENTATION; FEATURE-EXTRACTION; DAMAGE DETECTION; SWARM; CNN; DESIGN;
D O I：
10.1515/revneuro-2018-0050
中图分类号：
Q189 [神经科学];
学科分类号：
071006 ;
摘要：
Clustering is a vital task in magnetic resonance imaging (MRI) brain imaging and plays an important role in the reliability of brain disease detection, diagnosis, and effectiveness of the treatment. Clustering is used in processing and analysis of brain images for different tasks, including segmentation of brain regions and tissues (grey matter, white matter, and cerebrospinal fluid) and clustering of the atrophy in different parts of the brain. This paper presents a state-of-the-art review of brain MRI studies that use clustering techniques for different tasks.
引用
收藏
页码：31 / 44
页数：14
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