PURPOSE: Statistical shape analysis of anatomical structures plays an important role in many medical image analysis applications such as understanding the structural changes in anatomy in various stages of growth or disease. Establishing accurate correspondence across object populations is essential for such statistical shape analysis studies. METHODS: In this paper, we present an entropy-based correspondence framework for computing point-based correspondence among populations of surfaces in a groupwise manner. This robust framework is parameterization-free and computationally efficient. We review the core principles of this method as well as various extensions to deal effectively with surfaces of complex geometry and application-driven correspondence metrics. RESULTS: We apply our method to synthetic and biological datasets to illustrate the concepts proposed and compare the performance of our framework to existing techniques. CONCLUSIONS: Through the numerous extensions and variations presented here, we create a very flexible framework that can effectively handle objects of various topologies, multi-object complexes, open surfaces, and objects of complex geometry such as high-curvature regions or extremely thin features.
PURPOSE: Statistical shape analysis of anatomical structures plays an important role in many medical image analysis applications such as understanding the structural changes in anatomy in various stages of growth or disease. Establishing accurate correspondence across object populations is essential for such statistical shape analysis studies. METHODS: In this paper, we present an entropy-based correspondence framework for computing point-based correspondence among populations of surfaces in a groupwise manner. This robust framework is parameterization-free and computationally efficient. We review the core principles of this method as well as various extensions to deal effectively with surfaces of complex geometry and application-driven correspondence metrics. RESULTS: We apply our method to synthetic and biological datasets to illustrate the concepts proposed and compare the performance of our framework to existing techniques. CONCLUSIONS: Through the numerous extensions and variations presented here, we create a very flexible framework that can effectively handle objects of various topologies, multi-object complexes, open surfaces, and objects of complex geometry such as high-curvature regions or extremely thin features.
Authors: Martin A Styner; Kumar T Rajamani; Lutz-Peter Nolte; Gabriel Zsemlye; Gábor Székely; Chris J Taylor; Rhodri H Davies Journal: Inf Process Med Imaging Date: 2003-07
Authors: Ipek Oguz; Marc Niethammer; Josh Cates; Ross Whitaker; Thomas Fletcher; Clement Vachet; Martin Styner Journal: Inf Process Med Imaging Date: 2009
Authors: Anupama Goparaju; Krithika Iyer; Alexandre Bône; Nan Hu; Heath B Henninger; Andrew E Anderson; Stanley Durrleman; Matthijs Jacxsens; Alan Morris; Ibolya Csecs; Nassir Marrouche; Shireen Y Elhabian Journal: Med Image Anal Date: 2021-10-26 Impact factor: 8.545
Authors: Yue Liu; Dario J Englot; Victoria L Morgan; Warren D Taylor; Ying Wei; Ipek Oguz; Bennett A Landman; Ilwoo Lyu Journal: Proc SPIE Int Soc Opt Eng Date: 2021-02-15