Mosaliganti Kishore, Janoos Firdaus, Sharp Richard, Ridgway Randall, Machiraju Raghu, Huang Kun, Wenzel Pamela, deBruin Alain, Leone Gustavo, Saltz Joel
Department of Computer Science and Engineering, The Ohio State University, Columbus, OH 43210, USA.
IEEE Trans Med Imaging. 2007 Sep;26(9):1283-90. doi: 10.1109/TMI.2007.903570.
In this paper, we propose a technique for detecting pockets on a surface-of-interest. A sequence of propagating fronts converging to the target surface is used as the basis for inspection. We compute a correspondence function between the initial and the target surface. This leads to a natural definition of the local feature size measured as the evolution distance between mapped points. Surface pockets are then extracted as salient clusters embedded in the feature space. The level-set initialization also determines the scale-space of the extracted pockets. Results are presented on a case-study in which the focus is to chronicle the phenotyping differences in genetically modified mouse placenta. Our results are validated based on manually verified ground-truth.
