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Proceedings Paper

Extensible knowledge-based architecture for segmenting CT data
Author(s): Matthew S. Brown; Michael F. McNitt-Gray; Jonathan G. Goldin; Denise R. Aberle
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Paper Abstract

A knowledge-based system has been developed for segmenting computed tomography (CT) images. Its modular architecture includes an anatomical model, image processing engine, inference engine and blackboard. The model contains a priori knowledge of size, shape, X-ray attenuation and relative position of anatomical structures. This knowledge is used to constrain low-level segmentation routines. Model-derived constraints and segmented image objects are both transformed into a common feature space and posted on the blackboard. The inference engine then matches image to model objects, based on the constraints. The transformation to feature space allows the knowledge and image data representations to be independent. Thus a high-level model can be used, with data being stored in a frame-based semantic network. This modularity and explicit representation of knowledge allows for straightforward system extension. We initially demonstrate an application to lung segmentation in thoracic CT, with subsequent extension of the knowledge-base to include tumors within the lung fields. The anatomical model was later augmented to include basic brain anatomy including the skull and blood vessels, to allow automatic segmentation of vascular structures in CT angiograms for 3D rendering and visualization.

Paper Details

Date Published: 24 June 1998
PDF: 11 pages
Proc. SPIE 3338, Medical Imaging 1998: Image Processing, (24 June 1998); doi: 10.1117/12.310934
Show Author Affiliations
Matthew S. Brown, UCLA School of Medicine (United States)
Michael F. McNitt-Gray, UCLA School of Medicine (United States)
Jonathan G. Goldin, UCLA School of Medicine (United States)
Denise R. Aberle, UCLA School of Medicine (United States)

Published in SPIE Proceedings Vol. 3338:
Medical Imaging 1998: Image Processing
Kenneth M. Hanson, Editor(s)

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