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In this thesis, we focus on several segmentation problems arising in the area of structural and functional neuroimaging. Our solutions to these problems are based on a retrospective framework, wherein a complex segmentation procedure is divided into two simpler steps, initial segmentation and incorporation of prior information. While combining these steps might potentially lead to an optimal solution, we show that the simpler two-step approach can either be made equivalent to a combined procedure or achieve superior performance, due to simpler optimization. We first consider the problem of detecting activated regions in functional Magnetic Resonance Imaging (fMRI). While activated regions are typically large, smaller spurious activations caused by noise are likely to appear in the segmentation. These can be removed by cluster size thresholding. Though cluster size thresholding can be regarded as a method to reduce false positives, it affects the smaller true activations. We show that in the context of Markov Random Field (MRF) based segmentation, simple removal of small regions after the segmentation is not optimal. We propose a retrospective correction approach that allows the regions to grow before they are eliminated based on the cluster size. This approach finds the best modification (removal or growth) and achieves superior performance to that of the standard MRF-based segmentation. The second and the main problem considered in this thesis is skull stripping, i.e., the problem of separating the brain tissues (white matter, grey matter, cerebral spinal fluid) from the non-brain tissues (skull, scalp, eye sockets, neck tissues, etc.). Skull stripping performance suffers from the problem of narrow connections between brain and non-brain structures which usually results in preservation of significant amount of non-brain tissues. Many popular methods rely on iterative surface deformation to fit the brain boundary and tend to leave residual dura. We first approach the problem in a general framework of narrow connection removal. Here, we show that of all existing approaches, isoperimetric algorithm performs the best but can be sensitive to initialization. Instead, we propose a novel approach based on graph cuts that has beneficial features global optimality, speed, and possibility of fully automated implementation for many applications. We also ii ATTENTION: The Singapore Copyright Act applies to the use of this document. Nanyang Technological University Library

Retrospective techniques for segmentation of structural and functional MR brain images