Semi-automatic segmentation of the hippocampus using magnetic resonance images

Hajiesmaeili, Maryam (2014) Semi-automatic segmentation of the hippocampus using magnetic resonance images. (PhD thesis), Kingston University, .


The aim of this thesis is to investigate techniques for accurate segmentation of the hippocampus in order to measure the degree of atrophy associated with diseases such as Alzheimer’s, temporal lobe epilepsy, long-lasting traumatic stress and schizophrenia. To this end, specific algorithms and methodologies are developed to segment the hippocampus from structural magnetic resonance (MR) images, in combination with pre- and post-processing operations to improve robustness and accuracy. Segmentation efficiency is boosted by pre-processing the input image with a bias correcting spatial fuzzy c-means algorithm and a nonlocal mean filter to smooth the MRI dataset whilst preserving edges. A 3D level set method is used to segment the left and right hippocampi simultaneously. The thesis investigates the problem of initialisation of the level set algorithm, which must cope with some challenging characteristics of the hippocampus, such as the small size, wide range of internal intensities, narrow width, and shape variation. Due to intensity inhomogeneity, using a single seed region inside the hippocampus is prone to failure. Hence, alternative initialisation strategies are explored, such as using multiple initialisations in different sections (such as the head, body and tail) of the hippocampus and ‘tailored’ initialisation based on superquadrics. Accurate quantification of a segmented hippocampus can provide essential details for diagnosis, treatment planning, and follow-up comparisons. Hence, a post-processing approach to quantify the partial volume effect (PVE) for correction of the hippocampal volume is assessed. The method enables estimation of the PVE in order to generate more accurate measurements of the hippocampal volume. The results of segmentation are evaluated on two public MRI datasets that include annotated ground-truth to identify the hippocampus. Experimental results indicate that using a single initialisation results in an average correct segmentation of only 39%, though the performance rises to 85% when using the multiple initialisations approach. These results are shown to exceed the performance achieved by other researchers for these datasets. The analyses of corrected volumes of the several publicly available datasets are used to quantify the asymmetry in the size of the left and right hippocampi. The measure of asymmetry is applied to a set of normal scans and ones from epileptic patients. The average asymmetry values were 7% and 12% respectively, indicating asymmetry may be a useful index for diagnosis of diseases associated with the differential shrinkage of the hippocampus.

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