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.