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29 January 1999 Evaluation of prostate tumor grades by content-based image retrieval
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Proceedings Volume 3584, 27th AIPR Workshop: Advances in Computer-Assisted Recognition; (1999) https://doi.org/10.1117/12.339826
Event: The 27th AIPR Workshop: Advances in Computer-Assisted Recognition, 1998, Washington, DC, United States
Abstract
As part of collaboration between the Pittsburgh Supercomputing Center and the University of Pittsburgh Medical Center we are developing methods for content based image retrieval to assist pathology diagnosis. We have been using Gleason grading of prostate tumor samples as an initial domain for evaluating the effectiveness of the method for specific tasks. In this application, the system does not attempt to directly reproduce pathologists' visual analysis. Rather, it relies on the comparison of image features from a sample image to key the retrieval of similar but previously graded images from a database. Appropriate features should be highly selective to architecture differences of the Gleason system so the grades of the retrieved images can be applied to the unknown sample. We have been investigating the usefulness of computational geometry structures, such as spanning trees, as components of feature sets providing accurate retrieval of matching grades.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Arthur W. Wetzel, R. Crowley, Sujin Kim, R. Dawson, Lei Zheng, Y. M. Joo, Yukako Yagi, John Gilbertson, C. Gadd, D. W. Deerfield, and Michael J. Becich "Evaluation of prostate tumor grades by content-based image retrieval", Proc. SPIE 3584, 27th AIPR Workshop: Advances in Computer-Assisted Recognition, (29 January 1999); https://doi.org/10.1117/12.339826
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