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Proceedings Paper

Interactive classification and content-based retrieval of tissue images
Author(s): Selim Aksoy; Giovanni B. Marchisio; Carsten Tusk; Krzysztof Koperski
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Paper Abstract

We describe a system for interactive classification and retrieval of microscopic tissue images. Our system models tissues in pixel, region and image levels. Pixel level features are generated using unsupervised clustering of color and texture values. Region level features include shape information and statistics of pixel level feature values. Image level features include statistics and spatial relationships of regions. To reduce the gap between low-level features and high-level expert knowledge, we define the concept of prototype regions. The system learns the prototype regions in an image collection using model-based clustering and density estimation. Different tissue types are modeled using spatial relationships of these regions. Spatial relationships are represented by fuzzy membership functions. The system automatically selects significant relationships from training data and builds models which can also be updated using user relevance feedback. A Bayesian framework is used to classify tissues based on these models. Preliminary experiments show that the spatial relationship models we developed provide a flexible and powerful framework for classification and retrieval of tissue images.

Paper Details

Date Published: 21 November 2002
PDF: 11 pages
Proc. SPIE 4790, Applications of Digital Image Processing XXV, (21 November 2002); doi: 10.1117/12.453862
Show Author Affiliations
Selim Aksoy, Insightful Corp. (United States)
Giovanni B. Marchisio, Insightful Corp. (United States)
Carsten Tusk, Insightful Corp. (United States)
Krzysztof Koperski, Insightful Corp. (United States)


Published in SPIE Proceedings Vol. 4790:
Applications of Digital Image Processing XXV
Andrew G. Tescher, Editor(s)

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