Markov random field modeling in image analysis
Markov random field modeling in image analysis
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Markov Random Field model of microarray gridding
Proceedings of the 2003 ACM symposium on Applied computing
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
A deformable grid approach for Bayesian image registration
SPPRA '08 Proceedings of the Fifth IASTED International Conference on Signal Processing, Pattern Recognition and Applications
Constructing the histogram representation for automatic gridding of cDNA microarray images
ICMB'08 Proceedings of the 1st international conference on Medical biometrics
| Hi-index | 0.00 |
The paper reports a novel approach for the problem of automatic gridding in Microarray images. Such problem often requires human intervention; therefore, the development of automated procedures is a fundamental issue for large-scale functional genomic experiments involving many microarray images. Our method uses a two-step process. First a regular rectangular grid is superimposed on the image by interpolating a set of guide spots, this is done by solving a non-linear optimization process with a stochastic search producing the best interpolating grid parameterized by a six values vector. Second, the interpolating grid is adapted, with a Markov Chain Monte Carlo method, to local deformations. This is done by modeling the solution a Markov random field with a Gibbs prior possibly containing first order cliques (1-clique). The algorithm is completely automatic and no human intervention is required, it efficiently accounts arbitrary grid rotations, irregularities and various spot sizes.