Syracuse University

Hao Chen

Pramod K Varshney

Renbin Peng

James Michels

Apparatus and method for detecting micro-calcifications in mammograms using novel algorithms and stochastic resonance noise is provided, where a suitable dose of noise is added to the abnormal mammograms such that the performance of a suboptimal lesion detector is improved without altering the detector's parameters. A stochastic resonance noise-based detection approach is presented to improve suboptimal detectors which suffer from model mismatch due to the Gaussian assumption. Furthermore, a stochastic resonance noise-based detection enhancement framework is presented to deal with more general model mismatch cases.

Optimized stochastic resonance signal detection method

Apparatus and method for improving the detection of signals obscured by noise using stochastic resonance noise. The method determines the stochastic resonance noise probability density function in non-linear processing applications that is added to the observed data for optimal detection with no increase in probability of false alarm. The present invention has radar, sonar, signal processing (audio, image and video), communications, geophysical, environmental, and biomedical applications.

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Syracuse, NY, United States of America

Hao Chen