Brain Tumour Categorization with MRI Representation by Feature Extraction using GLCM and Support Vector Machine

Abstract

Cluster of tissue that influence the normal tissue by moderate expansion of irregular cells is known as Brain tumour. It happens when cell get anomalous development inside the brain and this remains the primary reason in increasing the fatality rate among humans. Among a wide range of cancers, brain tumour is incredibly serious and to avoid life threats of an individual, quick diagnosing and treatment must be given. Identifying these cells is a problematic issue, due to formation of tumour cells. It is exceptionally crucial to classify brain tumour from MRI image. In our proposed work, MRI images retrieved by utilizing Content based image retrieval technique are taken as input for classification. To achieve accuracy in classification and for efficient segmentation, pre-processing is carried for color conversion, noise reduction and resizing. Segmentation of tumour cells is done by Expectation and Maximization technique to know about region of affected cells present in that segmented area. This is trailed by statistical and size-based feature extraction by Gray Level Co-Occurrence Matrix from segmented images. The features extracted from segmented portion will be trained to analyze the presence of tumour in given MR images. The brain tumor classification, done in MATLAB environment allows localizing a mass of abnormal cells in a slice of MRI image using SVM Classifier, which is quickest method and furthermore give the great accuracy in classification. Experimental results accomplished accuracy of 100% in distinguishing the tissues as normal or abnormal from MR images exhibiting the viability of proposed method.