Automatic Image Analysis of Histopathology
Specimens Using Concave Vertex Graph
Lin Yang(1,3), Oncel Tuzel(2), Peter Meer(1), and David J. Foran(3)
(1)Department of Electrical and Computer Engineering
(2)Department of Computer Science
Rutgers University, Piscataway, NJ 08854, USA
(3)BioImaging Laboratory, Department of Pathology and Laboratory Medicine
UMDNJ-Robert Wood Johnson Medical School
Piscataway, NJ 08855, USA
Automatic image analysis of histopathology specimens would
help the early detection of blood cancer. The first step for automatic
image analysis is segmentation. However, touching cells bring the
difficulty
for traditional segmentation algorithms. In this paper, we propose
a novel algorithm which can reliably handle touching cells
segmentation.
Robust estimation and color active contour models are used to
delineate
the outer boundary. Concave points on the boundary and inner edges
are automatically detected. A concave vertex graph is constructed from
these points and edges. By minimizing a cost function based on
morphological
characteristics, we recursively calculate the optimal path in the
graph to separate the touching cells. The algorithm is computationally
efficient and has been tested on two large clinical dataset which
contain
207 images and 3898 images respectively. Our algorithm provides better
results than other studies reported in the recent literature.
11th International Conference on Medical Image Computing and
Computer Assisted Intervention (MICCAI) ,
New York City, USA, September 2008;
Springer, LNCS 5241, Part I, 833-841, 2008.
