Segmentation of medical imagery with pulse-coupled neural networks

This paper discusses some of the advantages and disadvantages of pulse-coupled neural networks (PCNNs) for performing image segmentation in the realm of medical diagnostics. PCNNs were tested with magnetic resonance imagery (MRI) of the brain and abdominal region and nuclear scintigraphic ventilation/perfusion 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 difficulty is finding the optimum parameter values so that a uniform response is achieved over a set of imagery. Also, a set of parameters that properly segments objects in one image is sometimes unsuccessful on a similar image