Performance Analysis of Supervised & Unsupervised Techniques for Brain Tumor Detection and Segmentation from MR Images

Abstract

Brain tumor detection and segmentation from the magnetic resonance images (MRI) is a difficult task as in the MR brain images, various tissues such as white matter, gray matter, and cerebrospinal fluid have complicated structures that make it difficult to segment the tumor. An automated system for brain tumor detection and segmentation will help the patients for proper treatment planning. Also, it will improve the diagnosis and reduce the diagnostic time. Segmentation of brain tumor MR images is the most difficult task as the tumor varies in terms of size, shape, location, and texture. In this paper, we discuss various supervised and unsupervised techniques for brain tumor detection and segmentation such as K-nearest neighbor (K-NN), K-means clustering, and using morphological operators. We also review the results obtained.