An Improved Type 2 Fuzzy C Means Clustering for MR Brain Image Segmentation based on Possibilistic Approach and Rough Set Theory

It is necessary to extract various attributes from an image especially in the field of neurological pathology. Magnetic Resonance Imaging (MRI) is a popularly used scanning technique for soft tissues like brain as it provides a detailed view of the tissue. It requires highly accurate segmentation algorithms to cluster a brain image into its constituent tissue regions. In consideration to this necessity, fuzzy set theory proves to be suitable to achieve tissue clustering on the brain MR images. However, the need to obtain better segmentation makes clustering efficiency more demanding. This fact encourages us to propose an advanced clustering algorithm known as Improved Rough Possibilistic Type-2 Fuzzy C Means that includes Skull Stripping and Median Filtering to enhance the performance. The proposed algorithm addresses various issues experienced by several other clustering algorithms and its superiority over them is quantitatively validated through authentic performance metrics like Jaccard Index, Accuracy and Adjusted Rand Index.