Abstract
Small animal imaging is the conjunctive ring between experimental research and clinical implementation. Positron Emission Tomography (PET) has proven a valuable tool for in vivo small animal functional imaging. Image reconstruction in PET uses the collected projection data of the object/patient under examination. The purpose of this study is to assess the performance of iterative reconstruction methods, using phantom data from a prototype small-animal PET system. The algorithms being compared are the simultaneous versions of ART (SART), EM-ML, ISRA and WLS and a new iterative algorithm being introduced under the short name ISWLS. In the second part of this thesis elastic or deformable models are studied. Various methods of parametric elastic models are presented, namely the classical snake, the gradient vector field snake (gvf-snake) and the topogy-adaptive snake (t-snake). Also presented the method of self-affine mapping system as an alternative of elastic models. Further a new comparison criterion for the self affine mapping system method is introduced. All methods are applied to retinal images with the purpose of segmenting the optical disk. Moreover the aforementioned methods are compared in terms of segmentation accuracy.
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