Prostate cancer is the second most common cause of cancer deaths and is the most frequently detected form of cancer of males in the US. Death rate scan be greatly reduced by early treatment. Consequently, it is important to understand the cause and progression of this disease in order to improve detection and treatment methods. As part of the Cancer Genome Anatomy Project work is underway to produce a 'molecular finger print' of prostate cancer.
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.
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