A Deformable Grid Matching Approach for Microarray Images

A fundamental step of microarray image analysis is the detection of the grid structure for the accurate location of each spot, representing the state of a given gene in a particular experimental condition. This step is known as gridding and belongs to the class of deformable grid matching problems which are well known in literature. Most of the available microarray gridding approaches require human intervention; for example, to specify landmarks, some points in the spot grid, or even to precisely locate individual spots. Automating this part of the process can allow high throughput analysis. This paper focuses on the development of a fully automated procedure for the problem of automatic microarray gridding. It is grounded on the Bayesian paradigm and on image analysis techniques. The procedure has two main steps. The first step, based on the Radon transform, is aimed at generating a grid hypothesis; the second step accounts for local grid deformations. The accuracy and properties of the procedure are quantitatively assessed over a set of synthetic and real images; the results are compared with well-known methods available from the literature.

L'articolo descrive un nuovo algoritmo per la caratterizzazione geometrica di immagini usate in Biologia Molecolare come i Microarray. Il problema affrontato si chiama "gridding" che consiste nel derivare le coordinate degli spot dell'immagine su una griglia discreta in maniera robusta e senza supervisione. L'articolo descrive un metodo basato sulla teoria Baiesiana delle decisioni, modellando la soluzione del problema come un Markov Random Field. La soluzione e' ottenuta minimizzando la distribuzione a posteriori delle soluzioni. L'algoritmo proposto e' stato ripreso da numero studi successive e da altri gruppi come riferimento per il gridding automatico di immagini da microarray.