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Segmentation of 3D microtomographic images of granular materials with the stochastic watershed
Author(s) -
FAESSEL M.,
JEULIN D.
Publication year - 2010
Publication title -
journal of microscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.569
H-Index - 111
eISSN - 1365-2818
pISSN - 0022-2720
DOI - 10.1111/j.1365-2818.2009.03349.x
Subject(s) - watershed , segmentation , artificial intelligence , computer science , computer vision , object (grammar) , image segmentation , parametric statistics , function (biology) , pattern recognition (psychology) , mathematics , statistics , evolutionary biology , biology
Summary Segmentation of 3D images of granular materials obtained by microtomography is not an easy task. Because of the conditions of acquisition and the nature of the media, the available images are not exploitable without a reliable method of extraction of the grains. The high connectivity in the medium, the disparity of the object's shape and the presence of image imperfections make classical segmentation methods (using image gradient and watershed constrained by markers) extremely difficult to perform efficiently. In this paper, we propose a non‐parametric method using the stochastic watershed, allowing to estimate a 3D probability map of contours. Procedures allowing to extract final segmentation from this function are then presented.