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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/15053

Title: Classification and Segmentation of Brain Tumor using Texture Analysis
Authors: Qurat-ul-Ain
Ghazanfar Latif
M. Arfan Jaffar
Anwar M. Mirza
Keywords: Segmentation, Classification, Texture feature, Magnetic resonance imaging (MRI), Support vector machine (SVM), Ensemble base classifier
Issue Date: 2010
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.
URI: http://hdl.handle.net/123456789/15053
Appears in Collections:College of Computer and Information Sciences

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