Date: December 14-16, 2014.
Source: 2014 ASE BigData/SocialInformatics/PASSAT/BioMedCom 2014 Conference, Harvard University.
Abstract: We describe a novel automatic computational framework for evaluating and visualizing the results of infant cranial surgeries. We begin by capturing a 3D triangle mesh of the subject’s head using a 3dMD camera. Our framework includes a mesh decimation algorithm to simplify these 3D meshes. Then we register mesh pairs and compare local features at each related vertex pair on pre-surgery and postsurgery meshes. Finally using false color, we visualize the difference between local geometric features before and after surgery. The goal of this visualization is to assist surgeons in evaluating the efficacy of their surgical techniques.
Article: A New Objective Automatic Computational Framework for Evaluating and Visualizing the Results of Infant Cranial Surgery.
Authors: Yuan, Binhang; Khechoyan, David; Goldman, Ron. Department of Computer Science, Rice University, Texas Children’s Hospital, Department of Surgery, Baylor College of University, Department of Computer Science, Rice University.