Anatomical structure similarity estimation by random forest

The morphological similarity of anatomical structures is essential to the study of the species evolution. In this paper, we investigate the unsupervised shape similarity analysis by a random-forest-based metric. The dense continuous deformation fields are employed as the shape descriptors. The forest is built when given the unlabeled deformation fields, where the leaves can be seen as an optimal clustering of the data set. The salient region is defined based on the dominant feature channels determined by the forests. The pairwise shape distance is computed efficiently with just binary comparisons stored in tree branches. We have applied our method to several skeletal data sets, including the skulls, teeth, radii, and metatarsals. Our experiments demonstrate the proposed method can handle the taxonomic classification effectively.