Fuzzy anisotropic diffusion based segmentation and texture based ensemble classification of brain tumor

• Proposed system combines feature extraction techniques with classification and segmentation techniques.
• Proposed system used ensemble base classifier with SVM as base classifier for the classification of brain images as normal or tumor.
• Genetic algorithm based weighted majority voting scheme has been used for ensemble classifiers.

Brain tumor is one of the major causes of death among other types of the cancers. Proper and timely diagnosis can prevent the life of a person to some extent. Therefore we have proposed an automated reliable system for the diagnosis of the brain tumor. Proposed system is a multi-stage system for brain tumor diagnosis and tumor region extraction. First, noise removal is performed as the preprocessing step on the brain MR images. Texture features are extracted from these noise free brain MR images. Next phase of the proposed system is classification that is based on these extracted features. Ensemble based SVM classification is used. More than 99% accuracy is achieved by the classification phase. After classification, proposed system extracts tumor region from tumorous images using multi-step segmentation. First step is skull removal and brain region extraction. Next step is separating tumor region from normal brain cells using FCM clustering. Results of the proposed technique show that tumor region is extracted quite accurately. This technique has been tested against the datasets of different patients received from Holy Family hospital and Abrar MRI & CT Scan center Rawalpindi.

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