Project Description:
Finding meaningful 1-1 correspondences between hippocampal (HP)
surfaces is an important but difficult problem in computational anatomy.
Unless high-field imaging is used, there are no well-defined anatomical features
on the HP that can be used as landmark constraints, so defining meaningful
registrations between HP surfaces is challenging. Here we developed a new
algorithm to automatically register HP surfaces with complete geometric
matching, avoiding the need to manually label landmark features. A good
registration depends on a reasonable choice of shape energy that measures
the dissimilarity between surfaces. In our algorithm, we first propose a
complete shape index using the Beltrami coefficient and curvatures, which
measures subtle local differences. The proposed shape energy is zero if
and only if two shapes are identical up to a rigid motion. We then seek the
best surface registration by minimizing the shape energy. We propose a simple
representation of surface diffeomorphisms using Beltrami coefficients, which
simplifies the optimization process. We then iteratively minimize the shape
energy using the Beltrami Holomorphic flow (BHF) method introduced in this
paper. Experimental results on 212 HP of normal and diseased (Alzheimer's
disease) subjects show our proposed algorithm is effective in registering
HP surfaces with complete geometric matching. The proposed shape energy can
also capture local shape differences between HP, for shape analysis in Alzheimer's
disease, schizophrenia and epilepsy.
Publication:
Result: