Development of Unsupervised Image Segmentation Schemes for Brain MRI using HMRF model

Image segmentation is a classical problem in computer vision and is of paramount importance to medical imaging. Medical image segmentation is an essential step for most subsequent image analysis task. The segmentation of anatomic structure in the brain plays a crucial role in neuro imaging analysis. The study of many brain disorders involves accurate tissue segmentation of brain magnetic resonance (MR) images. Manual segmentation of the brain tissues, namely white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) in MR images by an human expert is tedious for studies involving larger database. In addition, the lack of clearly defined edges between adjacent tissue classes deteriorates the significance of the analysis of the resulting segmentation. The segmentation is further complicated by the overlap of MR intensities of different tissue classes and by the presence of a spatially and smoothly varying intensity in-homogeneity. The prime objective of this dissertation is to develop strategies and methodologies for an automated brain MR image segmentation scheme.

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