Fully Automatic Brain Tumor Segmentation by Using Competitive EM and Graph Cut

被引:7
作者
Pedoia, Valentina [1 ]
Balbi, Sergio [2 ]
Binaghi, Elisabetta [3 ]
机构
[1] Univ Calif San Francisco, Dept Rad & Biomed Imaging, San Francisco, CA 94143 USA
[2] Insubria Univ, Dept Biotechnol & Life Sci, Varese, Italy
[3] Insubria Univ, Dept Theoret & Appl Sci, Varese, Italy
来源
IMAGE ANALYSIS AND PROCESSING - ICIAP 2015, PT I | 2015年 / 9279卷
关键词
MRI segmentation; Brain tumor; Graph cut; Competitive expectation maximization; ENERGY MINIMIZATION;
D O I
10.1007/978-3-319-23231-7_51
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Manual MRI brain tumor segmentation is a difficult and time consuming task which makes computer support highly desirable. This paper presents a hybrid brain tumor segmentation strategy characterized by the allied use of Graph Cut segmentation method and Competitive Expectation Maximization (CEM) algorithm. Experimental results were obtained by processing in-house collected data and public data from benchmark data sets. To see if the proposed method can be considered an alternative to contemporary methods, the results obtained were compared with those obtained by authors who undertook the Multi-modal Brain Tumor Segmentation challenge. The results obtained prove that the method is competitive with recently proposed approaches.
引用
收藏
页码:568 / 578
页数:11
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