  The auditory ossicles that are located in the middle ear are the smallest
bones in the human body. Their damage will result in hearing loss. It is
therefore important to be able to automatically diagnose ossicles' diseases
based on Computed Tomography (CT) 3D imaging. However CT images usually include
the whole head area, which is much larger than the bones of interest, thus the
localization of the ossicles, followed by segmentation, both play a significant
role in automatic diagnosis. The commonly employed local segmentation methods
require manually selected initial points, which is a highly time consuming
process. We therefore propose a completely automatic method to locate the
ossicles which requires neither templates, nor manual labels. It relies solely
on the connective properties of the auditory ossicles themselves, and their
relationship with the surrounding tissue fluid. For the segmentation task, we
define a novel energy function and obtain the shape of the ossicles from the 3D
CT image by minimizing this new energy. Compared to the state-of-the-art
methods which usually use the gradient operator and some normalization terms,
we propose to add a Ricci curvature term to the commonly employed energy
function. We compare our proposed method with the state-of-the-art methods and
show that the performance of discrete Forman-Ricci curvature is superior to the
others.
