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
Main Authors: Aitali, N., Cherradi, B., Bouattane, O., Youssfi, M., Raihani, A.
Format: Conference Proceeding
Language:English
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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Description
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.
ISSN:2159-1679
DOI:10.1109/ICM.2015.7438002