1 April 2002 Automatic determination of mass functions in Dempster-Shafer theory using fuzzy C-means and spatial neighborhood information for image segmentation
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Optical Engineering, 41(4), (2002). doi:10.1117/1.1457458
Abstract
The Dempster-Shafer (DS) evidence theory is a new approach to the problem of segmenting multimodal images coming from different sources. The performance of such segmentation scheme is, however, largely conditioned by the appropriate determination of mass functions in DS evidence theory. We present a method of automatically determining the mass function for image segmentation problems. The idea is to link, at the image pixel level, the notion of mass functions to that of membership functions in fuzzy logic. The mass assigned to a pixel is obtained from both the membership degree of the current pixel and those of its neighboring pixels. The membership degree of each pixel is determined by applying fuzzy c-means (FCM) clustering to the gray levels of the image. A method is presented to determine the simple or composite classes in DS evidence theory from the obtained membership degree. Final segmentation is achieved using the DS combination rule and decision. The developed mass function determination method is illustrated with both simulations and examples of physical images. We demonstrate the value of introducing fuzzy clustering in evidence theory for image segmentation.
Yue Min Zhu, Layachi Bentabet, Olivier Dupuis, Valerie Kaftandjian, Daniel Babot, Michele Rombaut, "Automatic determination of mass functions in Dempster-Shafer theory using fuzzy C-means and spatial neighborhood information for image segmentation," Optical Engineering 41(4), (1 April 2002). http://dx.doi.org/10.1117/1.1457458
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KEYWORDS
Image segmentation

Image fusion

Brain

Fuzzy logic

Composites

Tissues

X-ray imaging

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