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Proceedings Paper

Three applications of pulse-coupled neural networks and an optoelectronic hardware implementation
Author(s): Michele Ruggiero Banish; Heggere S. Ranganath; John R. Karpinsky; Rodney L. Clark; Glynn A. Germany; Philip G. Richards
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Paper Abstract

Pulse Coupled Neural Networks have been extended and modified to suit image segmentation applications. Previous research demonstrated the ability of a PCNN to ignore noisy variations in intensity and small spatial discontinuities in images that prove beneficial to image segmentation and image smoothing. This paper describes four research and development projects that relate to PCNN segmentation - three different signal processing applications and a CMOS integrated circuit implementation. The software for the diagnosis of Pulmonary Embolism from VQ lung scans uses PCNN in single burst mode for segmenting perfusion and ventilation images. The second project is attempting to detect ischemia by comparing 3D SPECT images of the heart obtained during stress and rest conditions, respectively. The third application is a space science project which deals with the study of global aurora images obtained from UV Imager. The paper also describes the hardware implementation of PCNN algorithm as an electro-optical chip.

Paper Details

Date Published: 9 March 1999
PDF: 8 pages
Proc. SPIE 3647, Applications of Artificial Neural Networks in Image Processing IV, (9 March 1999); doi: 10.1117/12.341118
Show Author Affiliations
Michele Ruggiero Banish, MemsOptical Inc. (United States)
Heggere S. Ranganath, Univ. of Alabama in Huntsville (United States)
John R. Karpinsky, MemsOptical Inc. (United States)
Rodney L. Clark, MemsOptical Inc. (United States)
Glynn A. Germany, Univ. of Alabama in Huntsville (United States)
Philip G. Richards, Univ. of Alabama in Huntsville (United States)

Published in SPIE Proceedings Vol. 3647:
Applications of Artificial Neural Networks in Image Processing IV
Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

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