An efficient method for constructing optimal statistical shape models

Abstract

Statistical shape models show considerable promise as a basis for segmenting and interpreting images. A major drawback of the approach is the need to establish a dense correspondence across a training set of segmented shapes. By posing the problem as one of minimising the description length of the model, we develop an efficient method that automatically defines a correspondence across a set of shapes. As the correspondence does not use an explicit ordering constraint, it generalises to 3D shapes. Results are given for several different training sets of 2D boundaries, showing the automatic method constructs better models than ones built by hand.