Hippocampus Localization Guided by Coherent Point Drift Registration Using Assembled Point Set

This paper presents a new approach for hippocampus localization using pairwise non-rigid Coherent Point Drift registration method. The concept of assembled point set is introduced, which is a combination of the available training point sets into a single data space that represents its distribution. Non-rigid Coherent Point Drift is then adapted to register the assembled point set with a randomly chosen base model for hippocampus localization. The primary focus of this work is on the computational intensiveness of the localization approach, in which the proposed localization approach using assembled point set is compared with an existing groupwise non-rigid Coherent Point Drift (GCPD) approach. The computation intensiveness of the proposed approach grows at a quadratic rate as compared with GCPD that grows at a cubic rate. The proposed approach is validated with hippocampus localization task using 40-datasets. The Root Mean Square (RMS) distance between the approximated hippocampus locations and the ground truth is within an acceptable average of 0.6957-mm.