Modeling of Front Evolution with Graph Cut Optimization
IEEE Int. Conference on Image Processing, Oct. 2007
H. Chang
Q. Yang
M. Auer
B. Parvin
ABSTRACT
We present a novel active contour model in which
the traditional gradient descent optimization is replaced by graph
cut optimization. The basic idea is to first define an energy
function according to curve evolution and then construct a graph
with well selected edge weights based on the objective energy
function, which is further optimized via graph cut algorithm. In
this fashion, our model shares advantages of both level set method
and graph cut algorithm, which are ``topological'' invariance,
computational efficiency, and immunity to being stuck in the local
minima. The model is validated on synthetic images, applied to
two-class segmentation problem, and compared with the traditional
active contour to demonstrate effectiveness of the technique.
Finally, the method is applied to samples imaged with transmission
electron microscopy that demonstrate complex textured patterns
corresponding subcellular regions and micro-anatomy.
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H. Chang