Segmentation is a vital role in medical image processing, where clustering technique widely used in medical application particularly for brain tumour detection in magnetic resonance imaging (MRI).We propose a novel automatic tumour segmentation method for MRI images. This method treats tumour segmentation as a classification problem. Additionally, the local independent projection-based classification (LIPC) method is used to classify each voxel into different classes. A novel classification framework is derived by introducing the local independent projection into the classical classification model. we applied adaptive mean shift algorithm to real MRI data and its performance is compared with other clustering methods including K-Means clustering and Fuzzy CMeans clustering. The transformation of an image into its set of features is known as feature extraction. It is a challenging task to extract good feature set for classification. Support vector machine (SVM) is one of the techniques used for the classification purpose .