00000cab a22000003a 4500 UP-99796217609548350 Buklod 20231007234357.0 m |o d | ta 101119s xx d | ||r ||||| || DENGII eng Suri, J.S. Shape recovery algorithms using level sets in 2-D 3-D medical imagery a state-of-the-art review. pp. 8-28 The class of geometric deformable models, also known as level sets, has brought tremendous impact to medical imagery due to its capability of topology preservation and fast shape recovery. In an effort to facilitate a clear and full understanding of these powerful state-of-the-art applied mathematical tools, the paper is an attempt to explore these geometric methods, their implementations and integration of regularizers to improve the robustness of these topologically independent propagating curves/surfaces. The paper first presents the origination of level sets, followed by the taxonomy of level sets. We then derive the fundamental equation of curve/surface evolution and zero-level curves/surfaces. The paper then focuses on the first core class of level sets, known as "level sets without regularizers." This class presents five prototypes: gradient, edge, area-minimization, curvature-dependent and application driven. The next section is devoted to second core class of level sets, known as "level sets with regularizers." In this class, we present four kinds: clustering-based, Bayesian bidirectional classifier-based, shape-based and coupled constrained-based. An entire section is dedicated to optimization and quantification techniques for shape recovery when used in the level set framework. Finally, the paper concludes with 22 general merits and four demerits on level sets and the future of level sets in medical image segmentation. We present applications of level sets to complex shapes like the human cortex acquired via MRI for neurological image analysis. 2D medical imagery. 3D medical imagery. Bayesian bidirectional classifier-based regularizers. MRI. Clustering-based regularizers. Coupled constrained-based regularizers. Curve/surface evolution. Geometric deformable models. Level sets. Neurological image analysis. Shape recovery algorithms. Shape-based regularizers. Topology preservation. IEEE Transactions on information technology in biomedicine 6, 1 (2002). FO UPD DENG-II Article