Date of Degree
PhD (Doctor of Philosophy)
Electrical and Computer Engineering
Accurate image segmentation is a challenging problem in the presence of weak boundary evidence, large object deformation, and serious mutual influence between multiple objects. In this thesis, we propose novel approaches to multi-object segmentation, which incorporates region, shape and context prior information to help overcome the stated challenges. The methods are based on a 3-D graph-theoretic framework. The main idea is to formulate the image segmentation problem as a discrete energy minimization problem. The prior region, shape and context information is incorporated by adding additional terms in our energy function , which are enforced using an arc-weighted graph representation. In particular, for optimal surface segmentation with region information, a ratio-form energy is employed, which contains both boundary term and regional term. To incorporate the shape and context prior information for multi-surface segmentation, additional shape-prior and context-prior terms are added, which penalize local shape change and local context change with respect to the prior shape model and the prior context model. We also propose a novel approach for the segmentation of terrain-like surfaces and regions with arbitrary topology. The context information is encoded by adding additional context term in the energy. Finally, a co-segmentation framework is proposed for tumor segmentation in PET-CT images, which makes use of the information from both modalities. The globally optimal solution for the segmentation of multiple objects can be obtained by computing a single maximum flow in a low-order polynomial time. The proposed method was validated on a variety of applications, including aorta segmentation in MRI images, intraretinal layer segmentation of OCT images, bladder-prostate segmentation in CT images, image resizing, robust delineation of pulmonary tumors in MVCBCT images, and co-segmentation of tumors in PET-CT images. The results demonstrated the applicability of the proposed approaches.
Copyright 2012 Qi Song
Song, Qi. "Globally optimal image segmentation incorporating region, shape prior and context information." dissertation, University of Iowa, 2012.