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Proceedings Paper

Medical image diagnoses by artificial neural networks with image correlation, wavelet transform, simulated annealing
Author(s): Harold H. Szu
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Paper Abstract

Classical artificial neural networks (ANN) and neurocomputing are reviewed for implementing a real time medical image diagnosis. An algorithm known as the self-reference matched filter that emulates the spatio-temporal integration ability of the human visual system might be utilized for multi-frame processing of medical imaging data. A Cauchy machine, implementing a fast simulated annealing schedule, can determine the degree of abnormality by the degree of orthogonality between the patient imagery and the class of features of healthy persons. An automatic inspection process based on multiple modality image sequences is simulated by incorporating the following new developments: (1) 1-D space-filling Peano curves to preserve the 2-D neighborhood pixels' relationship; (2) fast simulated Cauchy annealing for the global optimization of self-feature extraction; and (3) a mini-max energy function for the intra-inter cluster-segregation respectively useful for top-down ANN designs.

Paper Details

Date Published: 14 September 1993
PDF: 25 pages
Proc. SPIE 1898, Medical Imaging 1993: Image Processing, (14 September 1993); doi: 10.1117/12.154494
Show Author Affiliations
Harold H. Szu, Naval Surface Warfare Ctr. (United States)

Published in SPIE Proceedings Vol. 1898:
Medical Imaging 1993: Image Processing
Murray H. Loew, Editor(s)

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