New fine-grained clustering algorithm on GPU architecture for bias field correction and MRI image segmentation

In this paper, we propose a new fine-grained clustering bias field estimation and segmentation algorithm on Single Instruction Multiple Data (SIMD) architecture (GPU). The goal is to accelerate compute-intensive portions of the sequential version. We have implemented this parallel algorithm using Co...

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Bibliographic Details
Published in2015 27th International Conference on Microelectronics (ICM) pp. 118 - 121
Main Authors Aitali, N., Cherradi, B., Bouattane, O., Youssfi, M., Raihani, A.
Format Conference Proceeding Journal Article
LanguageEnglish
Published IEEE 01.12.2015
Subjects
Algorithms
Architecture
Bias
Bias field correction
Clustering
Clustering algorithms
Computer architecture
Conferences
CUDA
Gain
GPU
Graphics processing units
Image segmentation
Instruction sets
Kernel
Magnetic resonance imaging
Segmentation
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Summary:In this paper, we propose a new fine-grained clustering bias field estimation and segmentation algorithm on Single Instruction Multiple Data (SIMD) architecture (GPU). The goal is to accelerate compute-intensive portions of the sequential version. We have implemented this parallel algorithm using Compute Unified Device Architecture (CUDA) on different NVidia GPU cards. The numerical results in terms of execution time show a gain up to 52x for GTX 580 versus the sequential implementation.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Conference-1
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SourceType-Conference Papers & Proceedings-2
ISSN:2159-1679
DOI:10.1109/ICM.2015.7438002