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Brain tumour diagnostic segmentation based on optimal texture features and support vector machine classifier

Brain tumour diagnostic segmentation based on optimal texture features and support vector machine... This paper presents a new general automatic method for segmenting brain tumours in Magnetic Resonance (MR) images. Our approach addresses all types of brain tumours. The proposed method involves, therefore, image pre-processing, feature extraction via the wavelet transform-spatial gray level dependence matrix (WT-SGLDM), dimensionality reduction using the Genetic Algorithm (GA) and classification of the reduced features using a support vector machine (SVM). These optimal features are employed for the segmentation of brain tumour. The resulting method is aimed at early tumour diagnostics support by distinguishing between brain tissue, benign tumour tissue and malignant tumour tissue. The segmentation results in different types of brain tissues that are evaluated by comparison with manual segmentation, as well as with other existing techniques. The quantitative evaluation shows that our approach outperforms manual segmentation with match percent (MP) measures equal to 97.08% and 98.89% for malignant and the benign tumours, respectively. The qualitative evaluation displays that our attitude overtakes the FCM algorithm with an accuracy rate of 99.69% for benign tumours and 99.36% for malignant tumours. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Signal and Imaging Systems Engineering Inderscience Publishers

Brain tumour diagnostic segmentation based on optimal texture features and support vector machine classifier

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1748-0698
eISSN
1748-0701
DOI
10.1504/IJSISE.2014.060057
Publisher site
See Article on Publisher Site

Abstract

This paper presents a new general automatic method for segmenting brain tumours in Magnetic Resonance (MR) images. Our approach addresses all types of brain tumours. The proposed method involves, therefore, image pre-processing, feature extraction via the wavelet transform-spatial gray level dependence matrix (WT-SGLDM), dimensionality reduction using the Genetic Algorithm (GA) and classification of the reduced features using a support vector machine (SVM). These optimal features are employed for the segmentation of brain tumour. The resulting method is aimed at early tumour diagnostics support by distinguishing between brain tissue, benign tumour tissue and malignant tumour tissue. The segmentation results in different types of brain tissues that are evaluated by comparison with manual segmentation, as well as with other existing techniques. The quantitative evaluation shows that our approach outperforms manual segmentation with match percent (MP) measures equal to 97.08% and 98.89% for malignant and the benign tumours, respectively. The qualitative evaluation displays that our attitude overtakes the FCM algorithm with an accuracy rate of 99.69% for benign tumours and 99.36% for malignant tumours.

Journal

International Journal of Signal and Imaging Systems Engineering – Inderscience Publishers

Published: Jan 1, 2014

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