Pulse-coupled neural networks for medical image analysis

Paul E. Keller; A. David McKinnon

doi:10.1117/12.342900

22 March 1999 Pulse-coupled neural networks for medical image analysis

Paul E. Keller, A. David McKinnon

Proceedings Volume 3722, Applications and Science of Computational Intelligence II; (1999) https://doi.org/10.1117/12.342900
Event: AeroSense '99, 1999, Orlando, FL, United States

Abstract

Pulse-coupled neural networks (PCNNs) have recently become fashionable for image processing. This paper discusses some of the advantages and disadvantages of PCNNs for performing image segmentation in the realm of medical diagnostics. PCNNs were tested with magnetic resonance imagery (MRI) of the brian and abdominal region and nuclear scintigraphic imagery of the lungs (V/Q scans). Our preliminary results show that PCNNs do well at contrast enhancement. They also do well at image segmentation when each segment is approximately uniform in intensity. However, there are limits to what PCNNs can do. For example, when intensity significantly varies across a single segment, that segment does not properly separate from other objects. Another problem with the PCNN is properly setting the various parameters so that a uniform response is achieved over a set of imagery. Sometimes, a set of parameters that properly segment objects in one image fail on a similar image.

Citation Download Citation

Paul E. Keller and A. David McKinnon "Pulse-coupled neural networks for medical image analysis", Proc. SPIE 3722, Applications and Science of Computational Intelligence II, (22 March 1999); https://doi.org/10.1117/12.342900

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