Clustering-Based Detection of Anatomical Features on Organic Shapes

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. The proposed scheme was successfully evaluated on a large set of expert annotated shapes and showed superior performance compared to state-of-the-art surface analyzing methods. We achieve an increase in accuracy of 32 % and ensure the detection of all features.