Three applications of pulse-coupled neural networks and an optoelectronic hardware implementation

Michele Ruggiero Banish; Heggere S. Ranganath; John R. Karpinsky; Rodney L. Clark; Glynn A. Germany; Philip G. Richards

doi:10.1117/12.341118

9 March 1999 Three applications of pulse-coupled neural networks and an optoelectronic hardware implementation

Michele Ruggiero Banish, Heggere S. Ranganath, John R. Karpinsky, Rodney L. Clark, Glynn A. Germany, Philip G. Richards

Proceedings Volume 3647, Applications of Artificial Neural Networks in Image Processing IV; (1999) https://doi.org/10.1117/12.341118
Event: Electronic Imaging '99, 1999, San Jose, CA, United States

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.

Citation Download Citation

Michele Ruggiero Banish, Heggere S. Ranganath, John R. Karpinsky, Rodney L. Clark, Glynn A. Germany, and Philip G. Richards "Three applications of pulse-coupled neural networks and an optoelectronic hardware implementation", Proc. SPIE 3647, Applications of Artificial Neural Networks in Image Processing IV, (9 March 1999); https://doi.org/10.1117/12.341118

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KEYWORDS

Image segmentation

Neurons

Lung

Neural networks

Image processing

Auroras

Electro optical modeling

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