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Unsupervised segmentation based on robust estimation and color active contour models | IEEE Journals & Magazine | IEEE Xplore

Unsupervised segmentation based on robust estimation and color active contour models


Abstract:

One of the most commonly used clinical tests performed today is the routine evaluation of peripheral blood smears. In this paper, we investigate the design, development, ...Show More

Abstract:

One of the most commonly used clinical tests performed today is the routine evaluation of peripheral blood smears. In this paper, we investigate the design, development, and implementation of a robust color gradient vector flow (GVF) active contour model for performing segmentation, using a database of 1791 imaged cells. The algorithms developed for this research operate in Luv color space, and introduce a color gradient and L/sub 2/E robust estimation into the traditional GVF snake. The accuracy of the new model was compared with the segmentation results using a mean-shift approach, the traditional color GVF snake, and several other commonly used segmentation strategies. The unsupervised robust color snake with L/sub 2/E robust estimation was shown to provide results which were superior to the other unsupervised approaches, and was comparable with supervised segmentation, as judged by a panel of human experts.
Published in: IEEE Transactions on Information Technology in Biomedicine ( Volume: 9, Issue: 3, September 2005)
Page(s): 475 - 486
Date of Publication: 30 September 2005

ISSN Information:

PubMed ID: 16167702

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