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 of 1791 imaged cells. The algorithms developed for this research operate in Luv color space, and introduce a color gradient and L2E 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 L2E 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.
Yang
Lin
L
Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ 08854, USA. linyang@eden.rutgers.edu
Meer
Peter
P
Foran
David J
DJ
D000465:Algorithms; D001185:Artificial Intelligence; D003116:Color; D003124:Colorimetry / Q000379:methods*; D003198:Computer Simulation; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D018655:Lymphocyte Count / Q000379:methods*; D008214:Lymphocytes / Q000145:classification / Q000473:pathology*; D008232:Lymphoproliferative Disorders / Q000473:pathology*; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software; D014584:User-Computer Interface