Paper Info
Title
Prostate boundary detection in ultrasound images using biologically-inspired spiking neural network
Abstract
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.
Year
DOI
Venue
2011
10.1016/j.asoc.2010.07.001
Appl. Soft Comput.
Keywords
Field
DocType
ultrasound images,neural network,pcnn approach,pcnn sensitivity,pcnn parameter,prostate ultrasound image,published image processing algorithm,accurate boundary detection algorithm,various pcnn parameter,pcnn segmentation algorithm,prostate segmentation,pulse-coupled neural networks,overall boundary,original image,prostate boundary detection,bio-inspiring,image processing,median filter,machine learning,spiking neural network,ultrasound
Computer vision,Median filter,Segmentation,Computer science,Fuzzy logic,Data type,Artificial intelligence,Artificial neural network,Spiking neural network,Digital image processing,Machine learning,Ultrasound
Journal
Volume
Issue
ISSN
11
2
Applied Soft Computing Journal
Citations 
PageRank 
References 
12
0.94
15
Authors
3
Name
Order
Citations
PageRank
Aboul Ella Hassanien11610192.72
Hameed Al-Qaheri2329.31
El-Sayed A. El-Dahshan31387.69