Clustering-Based Detection of Anatomical Features on Organic Shapes

Abstract

Robust and accurate automatic detection of anatomical features on organic shapes is a challenging task. Despite a rough similarity, each shape is unique. To cope with this variety, we propose a novel clustering-based feature detection scheme. The scheme can be used as a standalone feature detection scheme or it can provide meaningful starting points for surface analyzing-based detection algorithms. The scheme includes the identification of a representative set of shapes and the usage of a specialized iterative closest point algorithm for the registration of shapes, which is followed by the projection of the features using the transformation matrix of the registration. Evaluation is based on a large set of expert annotated shapes and showed superior performance compared to state-of-the-art surface analyzing methods. Accuracy increased of 32% and detection of all features is ensure