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Micrographic Image Segmentation using LevelSetModel based on PossibilisticC-MeansClustering

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IEEE Xplore

Volume 2017, Issue 2017, 2017, Pages 188-192

Micrographic Image Segmentation using Level SetModel based on Possibilistic

C-MeansClustering

N. Chetih, N. Ramou, Z. MESSALIi, A. SERIR, Y. Boutiche

Abstract: Image segmentation is often required as afundamental stage in microstructure material characterization.The objective of this work is to choose hybridization betweenthe Level Set method and the clustering approach in order toextract the characteristics of the materials from thesegmentation result of the micrographic images. Morespecifically, the proposed approach contains two successivenecessary stages. The first one consists in the application ofpossibilistic c-means clustering approach (PCM) to get thevarious classes of the original image. The second stage isbased on using the result of the clustering approach i.e. oneclass among the three existing classes (which interests us) asan initial contour of the level set method to extract theboundaries of interest region.

The main purpose of using theresult of the PCM algorithm as initial step of the level setmethod is to enhance and facilitate the work of the latter. Ourexperimental results on real micrographic images show thatthe proposed segmentation method can extract successfully theinterest region according to the chosen class and confirm itsefficiency for segmenting micrographic images of materials.

Keywords : Level set, clustering approach, micrographic images;, image segmentation.

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