Classification and segmentation of brain tumor using texture analysis

Authors:
Qurat-Ul-Ain Qurat-Ul-Ain;Ghazanfar Latif;Sidra Batool Kazmi;M. Arfan Jaffar;Anwar M. Mirza
Affiliations:
Department of Computer Science, FAST National University of Computer and Emerging Sciences, Islamabad, Pakistan;Department of Computer Science, FAST National University of Computer and Emerging Sciences, Islamabad, Pakistan;Department of Computer Science, FAST National University of Computer and Emerging Sciences, Islamabad, Pakistan;Department of Computer Science, FAST National University of Computer and Emerging Sciences, Islamabad, Pakistan;Department of Computer Science, FAST National University of Computer and Emerging Sciences, Islamabad, Pakistan
Venue:
AIKED'10 Proceedings of the 9th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
Year:
2010

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Abstract

Brain tumor diagnosis is a very crucial task. This system provides an efficient and fast way for diagnosis of the brain tumor. Proposed system consists of multiple phases. First phase consists of texture feature extraction from brain MR images. Second phase classify brain images on the bases of these texture feature using ensemble base classifier. After classification tumor region is extracted from those images which are classified as malignant using two-stage segmentation process. Segmentation consists of skull removal and tumor extraction phases. Quantitative results show that our proposed system performed very efficiently and accurately. We achieved accuracy of classification beyond 99%. Segmentation results also show that brain tumor region is extracted quite accurately.