Segmentation of Carpal Bones from 3D CT Images  using  Skeletally Coupled Deformable Models

Abstract

The in vivo investigation of joint kinematics in normal and injured
wrist requires the segmentation of carpal bones from 3D (CT) images and
their registration over time. The non-uniformity of bone tissue, ranging
from dense cortical bone to textured spongy bone, the irregular, small
shape of closely packed carpal bones which move with respect to one another,
augmented with the presence of blood vessels, and the inherent blurring of
CT imaging renders the segmentation of carpal bones a challenging
task. Specifically, four characteristic difficulties are prominent:
(i) gaps or weak edges in the carpal bone surfaces, (ii) diffused
edges, (iii) textured regions, and (iv) extremely narrow inter-bone
regions. We review the performance of statistical classification,
deformable models, region growing, and morphological operations for this
application. We then propose a model which combines several of these approaches in one
framework. Specifically, initialized seeds grow in a curve evolution
implementation of active contours, but where growth is modulated by a
skeletally-mediated competition between neighboring regions, thus combining
the advantages of local and global region growing methods. This approach effectively
deals with many of the difficulties presented above as illustrated by
numerous examples.

Reference

Gzipped postscript file