

Structural Annotation of EM Images by Graph Cut
IEEE Int. Symp. on Biomedical Imagingfrom nano to macro , 2009
H. Chang
M. Auer
B. Parvin
ABSTRACT
Biological images have the potential to reveal complex
signatures that may not be amenable to morphological modeling in terms of shape, location, texture, and color. An effective analytical method is to characterize the composition
of a specimen based on userdefined patterns of texture and
contrast formation. However, such a simple requirement demands an improved model for stability and robustness. Here,
an interactive computational model is introduced for learning
patterns of interest by example. The learned patterns bound
an active contour model in which the traditional gradient descent optimization is replaced by the more efficient optimization of the graph cut methods. First, the energy function is
defined according to the curve evolution. Next, a graph is
constructed with weighted edges on the energy function and
is optimized with the graph cut algorithm. As a result, the
method combines the advantages of the level set method and
graph cut algorithm, i.e., "topological invariance" and computational efficiency. The technique is extended to the multi
phase segmentation problem; the method is validated on synthetic images and then applied to specimens imaged by transmission electron microscopy(TEM).
nuclei.
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