Concurrent computation of topological watershed on shared memory parallel machines |
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| Affiliation: | 1. Université Paris-Est, Laboratoire d''Informatique Gaspard-Monge, Equipe A3SI ESIEE Paris - Cité Descartes, BP99, 93162 Noisy Le Grand, France;2. Université Monastir, Laboratoire Technologie et Imagerie Médicale Faculté de Médecine de Monastir, Rue Ibn Sina 5019 Monastir, Tunisia;1. Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, Melaka 75450, Malaysia;2. Faculty of Engineering and Technology, Multimedia University, Jalan Ayer Keroh Lama, Melaka 75450, Malaysia;1. UMR 7198, Institut Jean Lamour, Université de Lorraine, Nancy, France;2. Laboratoire de Technologie et Imagerie Médicale, Université de Monastir, Monastir, Tunisia;3. Ecole Nationale d’Ingénieur de Sousse, Université de Sousse, Sousse, Tunisia |
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| Abstract: | The watershed transform is considered as the most appropriate method for image segmentation in the field of mathematical morphology. In the following paper, we present an adapted topological watershed algorithm suited for a rapid and effective implementation on Shared Memory Parallel Machine (SMPM). The introduced algorithm allows a parallel watershed computing while preserving the given topology. No prior minima extraction is needed, nor the use of any sorting step or hierarchical queue. The strategy that guides the parallel watershed computing, labeled SDM-Strategy (equivalent to Split-Distributes and Merge), is also presented. Experimental analyses such as execution time, performance enhancement, cache consumption, efficiency and scalability are also presented and discussed. |
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