MATLAB PROJECT
Narrow Gap Detection
in Microscope Images Using Marked Point Process Modeling
Abstract:
Differentiating objects separated by narrow
gaps is a challenging and important task in analyzing microscopic images. These
small separations provide useful information for applications that require
detailed boundary information and/or an accurate particle count. We present a
new approach to the modeling of these gaps based on a marked point process
(MPP) framework. We propose to model narrow gaps as geometric structures called
channels and define Gibbs energies for these models. The reversible-jump Markov
chain Monte Carlo (RJMCMC) algorithm embedded with simulated annealing is used
as an optimization method, and the switching kernel in an RJMCMC is newly
designed to speed up the algorithm. In this paper, we also propose a method to
exploit a detected channel configuration to reduce bridging channel defects in
conventional segmentation algorithms. The experimental results demonstrate that
the proposed channel modeling methods are successful in detecting gaps between
closely adjacent objects. The results also show that the proposed interaction
parameter control method improves boundary precision in the segmentation of
microscopic images. The implementation of this method is available at
https://engineering.purdue.edu/MASSI.
No comments:
Post a Comment