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Proceedings Paper

Knowledge representation for image-content analysis in medical image database
Author(s): Hui Luo; Roger S. Gaborski; Raj S. Acharya
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Paper Abstract

The object-oriented knowledge representation is considered as a natural and effective approach. Nevertheless, the use of object-oriented within complex image analysis has not undergone a rapid growth as it happened in other fields. We argue that one of the major problems comes from the difficulty of conceiving a comprehensive framework for coping with the different abstraction levels and the vision task operations. With the goal to overcome such a drawback, we present a new knowledge model for medical image content analysis based on the object-oriented paradigm. The new model abstracts common model for medical image content analysis based on the object-oriented paradigm. The new model abstracts common properties from different types of medical images by using three attribute parts: description, component, and semantic graph, and also specifies its actions to schedule the detection procedure, properly deform the shape of model components to match the corresponding anatomies in images, select the best match candidates, and verify combination graphs from detected candidates with the semantic graph defined in the model. The performance of the proposed model has been tested on pelvis digital radiographs. Initial results are encouraging.

Paper Details

Date Published: 3 July 2001
PDF: 11 pages
Proc. SPIE 4322, Medical Imaging 2001: Image Processing, (3 July 2001); doi: 10.1117/12.430977
Show Author Affiliations
Hui Luo, SUNY/Buffalo (United States)
Roger S. Gaborski, Rochester Institute of Technology (United States)
Raj S. Acharya, SUNY/Buffalo (United States)


Published in SPIE Proceedings Vol. 4322:
Medical Imaging 2001: Image Processing
Milan Sonka; Kenneth M. Hanson, Editor(s)

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