Paper Info
Title
New parallel hybrid implementation of bias correction fuzzy C-means algorithm
Abstract
In order to save patients with cerebral tumor disease, analysis and time processing of MRI brain images must be efficient, fast and relevant. The implementation of BCFCM algorithm on parallel graphics cards (GPUs) is an adequate remedy for the problem of processing time which can be elevated in urgent pathological cases. In this paper we present two implementations of Bias Correction Fuzzy C-means Algorithm using GPU card. Indeed we have already parallelized this algorithm, but this time we have enhanced the implementation, first by using the released mode instead of debug mode which is slow in execution time compared to release mode. Also, we have included the image edge pixels which were not the case in the previous work. Moreover, we have introduced and applied another method that gives interesting results compared to the other one. In the rest of this paper we will give the main steps of each implementation and then compare the new results in term of execution time and speedups.
Year
DOI
Venue
2017
10.1109/ATSIP.2017.8075519
2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)
Keywords
Field
DocType
BCFCM,Image segmentation,Parallel implementation,GPU
Graphics,Debug menu,Instruction set,Computer science,Fuzzy logic,Parallel computing,Algorithm,Implementation,Bias correction,Pixel,Cluster analysis
Conference
ISBN
Citations 
PageRank 
978-1-5386-0552-3
0
0.34
References 
Authors
7
5
Name
Order
Citations
PageRank
Noureddine Ait Ali110.70
Bouchaib Cherradi232.80
Ahmed El Abbassi310.70
Omar Bouattane41110.43
Mohamed Youssfi568.68