Medical image diagnoses by artificial neural networks with image correlation, wavelet transform, simulated annealing

Harold H. Szu

doi:10.1117/12.154494

14 September 1993 Medical image diagnoses by artificial neural networks with image correlation, wavelet transform, simulated annealing

Harold H. Szu

Author Affiliations +

Proceedings Volume 1898, Medical Imaging 1993: Image Processing; (1993) https://doi.org/10.1117/12.154494
Event: Medical Imaging 1993, 1993, Newport Beach, CA, United States

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.

Citation Download Citation

Harold H. Szu "Medical image diagnoses by artificial neural networks with image correlation, wavelet transform, simulated annealing", Proc. SPIE 1898, Medical Imaging 1993: Image Processing, (14 September 1993); https://doi.org/10.1117/12.154494

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