Topological features of an object are fundamental in image processing. In many applications,including medical imaging, it is important to maintain or control the topology of the image. Howeverthe design of such transformations that preserve topology and geometric characteristics of the inputimage is a complex task, especially in the case of parallel processing.Parallel processing is applied to accelerate computation by sharing the workload among multipleprocessors. In terms of algorithm design, parallel computing strategies profits from the naturalparallelism (called also partial order of algorithms) present in the algorithm which provides two main resources of parallelism: data and functional parallelism. Concerning architectural design, it is essential to link the spectacular evolution of parallel architectures and the parallel processing. In effect, if parallelization strategies become necessary, it is thanks to the considerable improvements in multiprocessing systems and the rise of multi-core processors. All these reasons make multiprocessing very practical. In the case of SMP machines, immediate sharing of data provides more flexibility in designing such strategies and exploiting data and functional parallelism, notably with the evolution of interconnection system between processors.In this perspective, we propose a new parallelization strategy, called SD&M (Split Distribute andMerge) strategy that cover a large class of topological operators. SD&M has been developed in orderto provide a parallel processing for many topological transformations.Based on this strategy, we proposed a series of parallel topological algorithm (new or adaptedversion). In the following we present our main contributions:(1)A new approach to compute watershed transform based on MSF transform, that is parallel,preserves the topology, does not need prior minima extraction and suited for SMP machines.Proposed algorithm makes use of Jean Cousty streaming approach and it does not require any sortingstep, or the use of any hierarchical queue. This contribution came after an intensive study of allexisting watershed transform in the discrete case.(2)A similar study on thinning transform was conducted. It concerns sixteen parallel thinningalgorithms that preserve topology. In addition to performance criteria, we introduce two qualitativecriteria, to compare and classify them. New classification criteria are based on the relationshipbetween the medial axis and the obtained homotopic skeleton. After this classification, we tried toget better results through the proposal of a new adapted version of Couprie's filtered thinningalgorithm by applying our strategy.(3)An enhanced computation method for topological smoothing through combining parallelcomputation of Euclidean Distance Transform using Meijster algorithm and parallel Thinning-Thickening processes using the adapted version of Couprie's algorithm already mentioned.
| Identifer | oai:union.ndltd.org:CCSD/oai:pastel.archives-ouvertes.fr:pastel-00680735 |
| Date | 13 December 2011 |
| Creators | Mahmoudi, Ramzi, Mahmoudi, Ramzi |
| Publisher | Université Paris-Est |
| Source Sets | CCSD theses-EN-ligne, France |
| Language | English |
| Detected Language | English |
| Type | PhD thesis |
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