Application of Symmetry Information in Magnetic Resonance Brain Image Segmentation
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Advances in neuroimaging techniques have facilitated the study of anatomical and functional changes in the brain. In order to assist precise diagnosis and treatment, automatic image analysis methods that provide quantitative measures are of great research interests. Accurate brain tissue segmentation of images has been one of the most important research areas for several years. It is an important initial step in neuroimage analysis for applications such as diagnosis of various brain diseases, treatment planning, and studies of various neurological disorders such as Alzheimer’s disease, Schizophrenia, and Multiple sclerosis (MS). However, all these potential applications are crucially dependent on the high accuracy of brain tissue segmentation. Accurate segmentation of MR brain images is difficult since these images contain various noise artifacts. Despite the extensive research, automated analysis of neuroimages still remains a challenging problem. Recently, attention has been turned towards integration of prior knowledge based on anatomical features to improve the accuracy. Based on the fact that the brain exhibits a high level of bilateral symmetry, in this thesis, I explore and discuss the importance of symmetry in the context of tissue classification in MRI, and develop a symmetry-based paradigm for automatic segmentation of brain tissues. Such a classification is motivated by potential radiological applications in assessing brain tissue volume, diagnosis of various brain diseases and treatment planning. The aim of this work is two-fold: First, identifying the location of the symmetry axis or, the symmetry plane becomes imperative. Accurate identification is crucial as it is valuable for the correction of possible misalignment of radiological scans and for symmetry evaluation. In the second stage, automatic classification of brain tissues is done. In other words, the first part of this research focuses on finding the symmetry axis/plane, and the second part develops a segmentation method based on symmetry information.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Information and Communication Technology
Item Access Status
Magnetic resonance (MR)