An Efficient Riemannian Statistical Shape Model using Differential Coordinates

Statistical models of shape have been established as one of the most successful methods for understanding the geometric variability of anatomical structures. Shape modeling is of particular interest in image guided diagnosis where morphological changes of anatomies have been hypothesized to be linked to various disorders. Based on a set of training shapes, statistical shape models efficiently parametrize the geometric variability of the biological objects under study. This in turn is not only useful in imposing shape constraints in synthesis and analysis problems but also in understanding the processes behind growth and disease.
Source: Medical Image Analysis - Category: Radiology Authors: Source Type: research