Analysis of Feature Extractor and Classifier for Magnetic Resonant Image Segmentation

Diagnostic imaging is a critical tool in healthcare sector. There are various modalities such as Magnetic resonance imaging (MRI), computed tomography (CT), digital mammography, and others, to provide an insight of subject's body, noninvasively in order to facilitate stakeholders to take decision in diagnosis. Additionally, in medical research, these technologies has been playing centre role in most of the health care studies and experiments. Being a critical component in imaging systems, MRI system has been active area for researchers in computational intelligence and image processing. One of the most important problems in image processing and analysis is segmentation and same is true for biomedical imaging. The main objective of segmentation is separating the pixels associated with different types of tissues like white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). In this paper, we present the analysis of various features to be used for segmentation process. Additionally, the classifiers such as SVM and Neural Network have also been compared here. The main objective of this paper is to determine the best candidate for the optimized feature and classifier to be used in segmentation process.