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Proceedings Paper

Constrained-optimization framework for detection of masses
Author(s): Galina L. Rogova; Chih-Chung Ke; Vivek Swarnakar; Raj S. Acharya; Paul C. Stomper
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Paper Abstract

Detection of abnormalities is critical to the success of mammogram screening and represents a perceptual problem even for experienced radiologists. This perceptual problem makes the development of reliable automated methods for detection of abnormalities very important. The present work demonstrates improvements in the existing techniques for detection of masses by using an evidential approach to mammogram segmentation. A method of partitioning mammograms into homogeneous regions by using `generic' label is presented. This method assigns the same label to regions based on similarity between regions in the feature space and does not require estimation of model parameters from specific region samples. The features best suited to represent the difference between tissue and masses texture are selected and combined within the framework of the Dempster-Shafer Theory of Evidence. Utilization of the Dempster-Shafer Theory of Evidence has improved the accuracy of detection by allowing to incorporate any number of different features and deal with the uncertainty inherent in these types of problems. Exploitation of constraints, representing domain knowledge, to forbid certain configuration of regions during segmentation results in an improved partitioning of the mammograms. A constrained stochastic relaxation algorithm is used for building an optimal label map to separate tissue and masses.

Paper Details

Date Published: 24 June 1998
PDF: 10 pages
Proc. SPIE 3338, Medical Imaging 1998: Image Processing, (24 June 1998); doi: 10.1117/12.310898
Show Author Affiliations
Galina L. Rogova, Calspan-UB Research Ctr. (United States)
Chih-Chung Ke, SUNY/Buffalo (United States)
Vivek Swarnakar, SUNY/Buffalo (United States)
Raj S. Acharya, SUNY/Buffalo (United States)
Paul C. Stomper, Roswell Park Cancer Institute (United States)

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

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