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J4  2012, Vol. 46 Issue (1): 170-176    DOI: 10.3785/j.issn.1008-973X.2012.01.27
Detecting MGMT expression status of glioma with magnetic
resonance image
LIU Chen-bin1, PAN Ying1, ZHANG Hai-shi2, HUANG Feng-ping2, XIA Shun-ren1
1. Key Laboratory of Biomedical Engineering, Ministry of Education, Zhejiang University, Hangzhou 310027, China;
2. Fudan University Affiliated Huashan Hospital, Shanghai 200040, China
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In order to overcome the deficiency of strong subjectivity in detecting O6-methylguanine-DNA methyltransferase (MGMT) expression of gliomas, an image processing method was proposed to analyze the magnetic resonance images (MRI) of Chinese glioma patients. The method included feature extraction, feature optimization and pattern recognition. Gray co-occurrence matrix, gray level-gradient co-occurrence matrix and two-dimensional discrete orthogonal S-transform (2D-DOST) were utilized to extract the texture features in the tumor area. Ring enhancement and age were also added in the initial feature set. Then k-nearest neighbor (KNN) and support vector machine (SVM) were combined to search optimal features. The optimal feature set was classified by SVM in a leave-one-out cross validation strategy (LOOCV). T1-weighted, T1-enhanced and FLAIR MRI of 25 glioma patients were analyzed. Results show that the algorithm can reduce the redundance of feature set, overcome the difficulty of small sample classification and identify the status of MGMT expression accurately and effectively.

Published: 22 February 2012
CLC:  TP 391.41  
Cite this article:

LIU Chen-bin, PAN Ying, ZHANG Hai-shi, HUANG Feng-ping, XIA Shun-ren. Detecting MGMT expression status of glioma with magnetic
resonance image. J4, 2012, 46(1): 170-176.

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