This work is concerned with the implementation of a fully 3D-consistent, automatic segmentation of bone structures in CT images. The morphological watersheds algorithm has been chosen as the base of the low-level segmentation. The over- segmentation, a phenomenon normally involved with this transformation, has been sorted out successfully by inserting modifying modules that act already within the algorithm. When dealing with a maxillofacial image, this approach also includes the possibility to provide two different divisions of the image: a fine-grained tessellation geared to the following high-level segmentation and a more coarse-grained one for the segmentation of the teeth. In the knowledge-based high-level segmentation, probabilistic considerations make use of specific properties of the 3D low-level regions to find the most probable tissue for each region. Low-level regions that cannot be classified with the necessary certainty are passed to a second stage, where--embedded in their respective environment--they are compared with structural patterns deduced from anatomical knowledge. The tooth segmentation takes the coarse-grained tessellation as its starting point. The few regions making up each tooth are grouped to 3D envelopes--one envelope per tooth. Matched filtering detects the bases of these envelopes. After a refinement they are fitted into the fine- grained, high-level segmented image.

Date Published: 21 May 1999
PDF: 10 pages
Proc. SPIE 3661, Medical Imaging 1999: Image Processing, (21 May 1999); doi: 10.1117/12.348582

Published in SPIE Proceedings Vol. 3661:
Medical Imaging 1999: Image Processing
Kenneth M. Hanson, Editor(s)