Automated segmentation of brain tumor using optimal texture features and support vector machine classifier

Authors:
Karim Gasmi;Ahmed Kharrat;Mohamed Ben Messaoud;Mohamed Abid
Affiliations:
CES Laboratory, University of Sfax, Sfax, Tunisia;CES Laboratory, University of Sfax, Sfax, Tunisia;ATMS Laboratory, University of Sfax, Sfax, Tunisia;CES Laboratory, University of Sfax, Sfax, Tunisia
Venue:
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II
Year:
2012

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Abstract

This paper presents a new general automatic method for segmenting brain tumors in magnetic resonance (MR) images. Our approach addresses all types of brain tumors. The proposed method involves, subsequently, image pre-processing, feature extraction via wavelet transform (WT), dimensionality reduction using genetic algorithm (GA) and classification of the extracted features using support vector machine (SVM). For the segmentation of brain tumor these optimal features are employed. The resulting method is aimed at early tumor diagnostics support by distinguishing between the brain tissue, benign tumor and malignant tumor tissue. The segmentation results on different types of brain tissue are evaluated by comparison with manual segmentation as well as with other existing techniques.