Microarray image gridding with stochastic search based approaches

Giuliano Antoniol, Michele Ceccarelli

Research output: Contribution to journalArticle

18 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)155-163
Number of pages9
JournalImage and Vision Computing
Issue number2
Publication statusPublished - Feb 2007



  • Genetic algorithm
  • Gridding
  • Markov random fields
  • Microarray

ASJC Scopus subject areas

  • Signal Processing
  • Computer Vision and Pattern Recognition

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