Gaussian mixture model based segmentation methods for brain MRI images

被引：53
|
作者：
Balafar, M. A. [1 ]
机构：
[1] Univ Tabriz, Dept Comp, Fac Engn, Tabriz, East Azerbaijan, Iran
来源：
ARTIFICIAL INTELLIGENCE REVIEW | 2014年 / 41卷 / 03期
关键词：
Statistical; MRI; Brain; ROBUST PARAMETER-ESTIMATION; PARTIAL VOLUME SEGMENTATION; AUTOMATIC SEGMENTATION; TISSUE CLASSIFICATION; UNIFYING FRAMEWORK; SPEEDING-UP; FUZZY; DISTRIBUTIONS; VALIDATION; ALGORITHM;
D O I：
10.1007/s10462-012-9317-3
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 at a preliminary stage of inclusion in diagnosis tools and the accurate segmentation of brain MRI images is crucial for a correct diagnosis by these tools. Due to in-homogeneity, low contrast, noise and inequality of content with semantic; brain MRI image segmentation is a challenging job. A review of the Gaussian Mixture Model based segmentation algorithms for brain MRI images is presented. The review covers algorithms for segmentation algorithms and their comparative evaluations based on reported results.
引用
收藏
页码：429 / 439
页数：11
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