Segmentation and Classification of Brain MRI Images Using Improved Logismos-B Algorithm

Automated reconstruction and diagnosis of brain MRI images is one of the most challenging problems in medical imaging. Accurate segmentation of MRI images is a key step in contouring during radiotherapy analysis. Computed tomography (CT) and Magnetic resonance (MR) imaging are the most widely used radiographic techniques in diagnosis and treatment planning. Segmentation techniques used for the brain Magnetic Resonance Imaging (MRI) is one of the methods used by the radiographer to detect any abnormality specifically in brain. The method also identifies important regions in brain such as white matter (WM), gray matter (GM) and cerebrospinal fluid spaces (CSF). These regions are significant for physician or radiographer to analyze and diagnose the disease. We propose a novel clustering algorithm, improved LOGISMOS-B to classify tissue regions based on probabilistic tissue classification, generalized gradient vector flows with cost and distance function. The LOGISMOS graph segmentation framework. Expand the framework to allow regionally-aware graph construction and segmentation.