2 research outputs found
Ricci Curvature Based Volumetric Segmentation of the Auditory Ossicles
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
Topology-Preserving 3D Image Segmentation Based On Hyperelastic Regularization
Image segmentation is to extract meaningful objects from a given image. For
degraded images due to occlusions, obscurities or noises, the accuracy of the
segmentation result can be severely affected. To alleviate this problem, prior
information about the target object is usually introduced. In [10], a
topology-preserving registration-based segmentation model was proposed, which
is restricted to segment 2D images only. In this paper, we propose a novel 3D
topology-preserving registration-based segmentation model with the hyperelastic
regularization, which can handle both 2D and 3D images. The existence of the
solution of the proposed model is established. We also propose a converging
iterative scheme to solve the proposed model. Numerical experiments have been
carried out on the synthetic and real images, which demonstrate the
effectiveness of our proposed model.Comment: 27 page