Hierarchical-based parallel technique for HMM 3D MRI brain segmentation algorithm

This paper proposes a hidden Markov model (HMM) algorithm for 3D MRI brain segmentation using a hierarchical/multi-level parallel implementation. The new technique is implemented using standard message passing interface (MPI). Two platforms are used to test the proposed technique namely PC-cluster s...

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Bibliographic Details
Published in:International journal of parallel, emergent and distributed systems emergent and distributed systems, 2012-08, Vol.27 (4), p.297-316
Main Authors: El-Moursy, Ali, Saif, Sheif, Younis, Akmal
Format: Article
Language:English
Subjects:
Algorithms
biomedical imaging
Brain
cluster computing
Clusters
Communication networks
high performance computing
High speed
Mathematical models
parallel algorithm
performance analysis
Segmentation
Studies
Three dimensional
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Summary:This paper proposes a hidden Markov model (HMM) algorithm for 3D MRI brain segmentation using a hierarchical/multi-level parallel implementation. The new technique is implemented using standard message passing interface (MPI). Two platforms are used to test the proposed technique namely PC-cluster system and IBM Blue Gene (BG)/L system. On PC-cluster system, hierarchical-based parallel HMM algorithm achieves a twofold speedup on a three nodes cluster and a threefold speedup on a six nodes cluster. Communication overhead and data dependency nullify any speedup beyond six nodes. On IBM BG/L system, the high-speed communication network and optimised MPI allow more efficient processing nodes utilisation although the algorithm data dependency limits the net speedup achieved.
ISSN:1744-5760
1744-5779
DOI:10.1080/17445760.2012.662681