Microarray image segmentation using marked point processes [articol]

Abstract

This paper presents a new method dedicated to unsupervised segmentation of spots in cDNA type microarray images. The framework relies on a marked point process algorithm. We shall create random circular objects to fit the spot distribution in the image. The interaction rules between the objects complete the model. Using a Markov Chain Monte Carlo (MCMC) method, the algorithm converges to a configuration which is close to the spot distribution in the images. At each step, the configuration is evaluated considering its proximity to the target distribution. In order to achieve this task, we propose a data model using a Gaussian gray level distribution and gradient detection to evaluate the likelihood of the current configuration. Finally, the results on the microarray images illustrate the efficiency of the segmentation and suggest that the marked point processes can be a promising tool for spot detection.