Image Processing Using Pulse-Coupled Neural Networks
Image Processing Using Pulse-Coupled Neural Networks
Image Segmentation by Networks of Spiking Neurons
Neural Computation
Ultrasound image guided patient setup for prostate cancer conformal radiotherapy
Pattern Recognition Letters
Review article: Review of pulse-coupled neural networks
Image and Vision Computing
Automatic Image Segmentation Algorithm Based on PCNN and Fuzzy Mutual Information
CIT '09 Proceedings of the 2009 Ninth IEEE International Conference on Computer and Information Technology - Volume 02
Rough sets and near sets in medical imaging: a review
IEEE Transactions on Information Technology in Biomedicine - Special section on body sensor networks
Intelligence techniques for prostate ultrasound image analysis
International Journal of Hybrid Intelligent Systems
Near sets: toward approximation space-based object recognition
RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
Tolerance near sets and image correspondence
International Journal of Bio-Inspired Computation
A new algorithm of multi-modality medical image fusion based on pulse-coupled neural networks
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
IEEE Transactions on Neural Networks
Image shadow removal using pulse coupled neural network
IEEE Transactions on Neural Networks
Rough matroids based on relations
Information Sciences: an International Journal
Computer Methods and Programs in Biomedicine
An efficient neural network based method for medical image segmentation
Computers in Biology and Medicine
Review: Pulse coupled neural networks and its applications
Expert Systems with Applications: An International Journal
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Pulse-coupled neural networks (PCNNs) are a biologically inspired type of neural networks. It is a simplified model of the cat's visual cortex with local connections to other neurons. PCNN has the ability to extract edges, segments and texture information from images. Only a few changes to the PCNN parameters are necessary for effective operation on different types of data. This is an advantage over published image processing algorithms that generally require information about the target before they are effective. 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. The experimental results obtained show that the overall boundary detection overlap accuracy offered by the employed PCNN approach is high compared with other machine learning techniques including Fuzzy C-mean and Fuzzy Type-II.