Narrow Gap Detection in Microscope Images Using Marked Point Process Modeling
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.