Unsupervised segmentation based on
robust estimation and color active contour models.
Lin Yang(1),(2), Peter Meer(1), David Foran(2)
(1)Department of Electrical and Computer Engineering
Rutgers University, Piscataway, NJ 08854, USA
(2)BioImaging Laboratory, Department of Pathology and Laboratory Medicine
UMDNJ-Robert Wood Johnson Medical School
Piscataway, NJ 08855, USA
One of the most commonly utilized 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 GVF active contour model for performing segmentation using a database
of 1,791 imaged cells. The algorithms developed for this research
operate in
Luv color space and introduce a color gradient and L_{2}E robust
estimation into the traditional GVF snake. The accuracy of the new
model
was compared with the segmentation results utilizing a mean-shift
approach, the traditional color GVF snake and several other commonly
utilized segmentation strategies. The unsupervised robust color snake
with L_{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.
IEEE Trans. on Information Technology in Biomedicine,
9, 475-486, 2005.
