Unsupervised segmentation based on robust estimation and color active contour models

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...

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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2005-09, Vol.9 (3), p.475-486
Main Authors: Lin Yang, Meer, P., Foran, D.J.
Format: Article
Language:English
Subjects:
Active contours
Algorithms
Artificial Intelligence
Biomedical imaging
Biomedical informatics
Blood
Color
Colorimetry - methods
Computer Simulation
Humans
Image databases
Image Interpretation, Computer-Assisted - methods
Image segmentation
Lymphocyte Count - methods
Lymphocytes - classification
Lymphocytes - pathology
Lymphoproliferative Disorders - pathology
Medical diagnostic imaging
Medical treatment
Models, Biological
Pattern Recognition, Automated - methods
Rendering (computer graphics)
Reproducibility of Results
Robustness
Sensitivity and Specificity
Software
unsupervised segmentation
User-Computer Interface
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Summary: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.
ISSN:1089-7771
2168-2194
1558-0032
2168-2208
DOI:10.1109/TITB.2005.847515