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Automated Segmentation of Brain Tumor Using Optimal Texture Features and Support Vector Machine Classifier

  • Karim Gasmi
  • Ahmed Kharrat
  • Mohamed Ben Messaoud
  • Mohamed Abid
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7325)

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.

Keywords

Image pre-processing Wavelet transform Genetic algorithm support vector machine segmentation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Karim Gasmi
    • 1
  • Ahmed Kharrat
    • 1
  • Mohamed Ben Messaoud
    • 2
  • Mohamed Abid
    • 1
  1. 1.CES LaboratoryUniversity of SfaxSfaxTunisia
  2. 2.ATMS LaboratoryUniversity of SfaxSfaxTunisia

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