Accurate detection of prostate boundary in ultrasound images using biologically-inspired spiking neural network

The main aim of this paper is to provide an accurate boundary detection algorithm of the prostate ultrasound images to assist radiologists in making their decisions. To increase the contrast of the ultrasound prostate image, the intensity values of the original images were adjusted firstly using the PCNN with median filter. It is followed by the PCNN segmentation algorithm to detect the boundary of the image. Combining adjusting and segmentation enable us to eliminate PCNN sensitivity to the setting of the various PCNN parameters whose optimal selection can be difficult and can vary even for the same problem. Analysis and experimental results show that the best segmentation output can be drawn from the simple and sophisticated ultrasound images using the spiking neural networks.