A new approach for segmentation of nuclei observed with an
epi-fluorescence microscope is presented. The technique is
model based and uses local feature activities such as
step-edge segments, roof-edge segments, and concave
corners to construct a set of initial hypotheses.
These local-feature activities are extracted using either local
or global operators to form a possible set of hypotheses.
Each hypothesis is expressed as a hyperquadric for better
stability, compactness, and error handling. The search space
is expressed as an assignment matrix with an appropriate
cost function to ensure local, adjacency, and global consistency.
Each possible configuration of a set of nuclei defines
a path, and the path with the least error corresponds to best
representation. This result is then presented to an operator who
verifies and eliminates a small number of errors.
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Publication number: LBNL-43106