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.
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