A0697
Title: Geometric and statistical models of analyzing two-object complexes
Authors: Zhiyuan Liu - University of North Carolina at Chapel Hill (United States) [presenting]
Abstract: The shape correlation of multi-object complexes in the human body is important for understanding the development of disease. The development of autism, for example, often changes the shapes of multiple brain structures. While there exist many statistical methods that can extract correlation from multi-block data, very little research can effectively extract intrinsic shape correlation. It is especially difficult to extract shape correlation when the involved objects have different variability in separate non-Euclidean spaces. Moreover, it is difficult to capture intrinsic shape information within and between objects for a joint analysis of multi-object complexes. Geometric and statistical models are presented that can extract shape correlation from two-object complexes. These models are designed to be insensitive to different variability of objects. Also, the results can be straightforwardly interpreted by researchers and clinical users.
