A fundamental step of Microarray image analysis is the detection of the grid structure for the accurate localization of each spot, representing the state of a given gene in a particular experimental condition. This step is known as gridding or microarray addressing. 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 [11]. This paper is aimed towards at the development fully automated procedures for the problem of automatic microarray gridding. Indeed, many of the automatic gridding approaches are based on two phases, the first aimed at the generation of an hypothesis consisting into a regular interpolating grid, whereas the second performs an adaptation of the hypothesis. Here we show that the first step can efficiently be accomplished by using the the Radon Transform, whereas the second step could be modeled by an iterative posterior maximization procedure [2]

Microarray image addressing based on the Radon transform

CECCARELLI M;
2005

Abstract

A fundamental step of Microarray image analysis is the detection of the grid structure for the accurate localization of each spot, representing the state of a given gene in a particular experimental condition. This step is known as gridding or microarray addressing. 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 [11]. This paper is aimed towards at the development fully automated procedures for the problem of automatic microarray gridding. Indeed, many of the automatic gridding approaches are based on two phases, the first aimed at the generation of an hypothesis consisting into a regular interpolating grid, whereas the second performs an adaptation of the hypothesis. Here we show that the first step can efficiently be accomplished by using the the Radon Transform, whereas the second step could be modeled by an iterative posterior maximization procedure [2]
978-078039134-5
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.12070/12176
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