Constrained-optimization framework for detection of masses

Galina L. Rogova; Chih-Chung Ke; Vivek Swarnakar; Raj S. Acharya; Paul C. Stomper

doi:10.1117/12.310898

24 June 1998 Constrained-optimization framework for detection of masses

Galina L. Rogova, Chih-Chung Ke, Vivek Swarnakar, Raj S. Acharya, Paul C. Stomper

Proceedings Volume 3338, Medical Imaging 1998: Image Processing; (1998) https://doi.org/10.1117/12.310898
Event: Medical Imaging '98, 1998, San Diego, CA, United States

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.

Citation Download Citation

Galina L. Rogova, Chih-Chung Ke, Vivek Swarnakar, Raj S. Acharya, and Paul C. Stomper "Constrained-optimization framework for detection of masses", Proc. SPIE 3338, Medical Imaging 1998: Image Processing, (24 June 1998); https://doi.org/10.1117/12.310898

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