Probabilistic segmentation and intensity estimation for microarray images

Gottardo, Raphaël; Besag, Julian; Stephens, Matthew; Murua, Alejandro

doi:10.1093/biostatistics/kxi042

Cited by 24 publications

(25 citation statements)

References 25 publications

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“…Image analysis is an important application area for HMRFs, the work by Geman and Geman (1984) and Besag (1986) having been influential in this. Recent application areas include micro-array data analysis (Gottardo et al 2006), brain imaging (Smith and Fahrmeir 2007), disease mapping (Green and Richardson 2002) and agricultural field experiments (Besag and Higdon 1999). The Ising model for representing binary lattice data and its generalisation to categorical data, the Potts model, are two well-known and widely applied examples of an MRF-type model.…”

Section: Introductionmentioning

confidence: 99%

Variational Bayes for estimating the parameters of a hidden Potts model

et al. 2008

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Hidden Markov random field models provide an appealing representation of images and other spatial problems. The drawback is that inference is not straightforward for these models as the normalisation constant for the likelihood is generally intractable except for very small observation sets. Variational methods are an emerging tool for Bayesian inference and they have already been successfully applied in other contexts. Focusing on the particular case of a hidden Potts model with Gaussian noise, we show how variational Bayesian methods can be applied to hidden Markov random field inference. To tackle the obstacle of the intractable normalising constant for the likelihood, we explore alternative estimation approaches for incorporation into the variational Bayes algorithm. We consider a pseudolikelihood approach as well as the more recent reduced dependence approximation of the normalisation constant. To illustrate the effectiveness of these approaches we present empirical results from the analysis of simulated datasets. We also analyse a real dataset and compare results with those of previous analyses as well as those obtained from the recently developed auxiliary variable MCMC method and the recursive MCMC method. Our results show that the variational Bayesian analyses can be carried out much faster than the MCMC analyses and produce good estimates of model parameters. We also found that the reduced dependence approximation of the normalisation constant outperformed the

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Section: Introductionmentioning

confidence: 99%

Variational Bayes for estimating the parameters of a hidden Potts model

et al. 2008

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“…Recently it has been shown that segmentation methods can significantly influence microarray data precision (Ahmed et al, 2004). Successful work on spot location and segmentation has already been done during the last years (Chen et al, 1997;Steinfath et al, 2001;Bozinov and Rahnenführer J, 2002;Yang et al, 2002;Demirkaya et al, 2005;Gottardo et al, 2006). A comparative evaluation of performance can be found in (Lehmussola et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Polar Modelling and Segmentation of Genomic Microarray Spots Using Mathematical Morphology

Angulo¹

2011

Image Anal Stereol

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Robust image analysis of spots in microarrays (quality control + spot segmentation + quantification) is a requirement for automated software which is of fundamental importance for a high-throughput analysis of genomics microarray-based data. This paper deals with the development of model-based image processing algorithms for qualifying/segmenting/quantifying adaptively each spot according to its morphology. A series of morphological models for spot intensities are introduced. The spot typologies represent most of the possible qualitative cases identified from a large database (different routines, techniques, etc.). Then, based on these spot models, a classification framework has been developed. The spot feature extraction and classification (without segmenting) is based on converting the spot image to polar coordinates and, after computing the radial/angular projections, the calculation of granulometric curves and derived parameters from these projections. Spot contour segmentation can also be solved by working in polar coordinates, calculating the up/down minimal path, which is easily obtained with the generalized distance function. With this model-based technique, the segmentation can be regularised by controlling different elements of the algorithm. According to the spot typology (e.g., doughnut-like or egg-like spots), several minimal paths can be computed to obtain a multi-region segmentation. Moreover, this segmentation is more robust and sensible to weak spots, improving the previous approaches.

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“…Although the correlation statistics is useful in flagging low-intensity spots, this method can perform well only on good-quality images. Gottardo et al [25] has applied a Markov random field approach to segmentation. In their method the red and green intensities for both foreground and background were represented using an uncorrelated bivariate t distribution.…”

Section: Introductionmentioning

confidence: 99%

3-D Spot Modeling for Automatic Segmentation of cDNA Microarray Images

Zacharia

Maroulis

2010

IEEE Trans.on Nanobioscience

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Abstract-Spot segmentation-the second essential stage of cDNA microarray image analysis-constitutes a challenging process. At present, several up-to-date spot-segmentation techniques or software programs-proposed in the literature-are often characterized as "automatic." On the contrary, they are in effect not fully automatic since they require human intervention in order to specify mandatory input parameters or to correct their results. Human intervention, however, can inevitably modify the actual results of the cDNA microarray experiment and lead to erroneous biological conclusions. Therefore, the development of an automated spot-segmentation process becomes of exceptional interest. In this paper, an original and fully automatic approach to accurately segmenting the spots in a cDNA microarray image is presented. In order for the segmentation to be accomplished, each real spot of the cDNA microarray image is represented in a three-dimensional (3-D) space by a 3-D spot model. Each 3-D spot model is determined via an optimization problem, which is solved by using a genetic algorithm. The segmentation of real spots is conducted by drawing the contours of their 3-D spot models. The proposed method has been compared with various published and established techniques, using several synthetic and real cDNA microarray images that contain thousands of spots. The outcome has shown that the proposed method outperforms prevalent existing techniques. It is also noise resistant and yields excellent results even under adverse conditions such as the appearance of spots of various sizes and shapes.

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Probabilistic segmentation and intensity estimation for microarray images

Cited by 24 publications

References 25 publications

Variational Bayes for estimating the parameters of a hidden Potts model

Variational Bayes for estimating the parameters of a hidden Potts model

Polar Modelling and Segmentation of Genomic Microarray Spots Using Mathematical Morphology

3-D Spot Modeling for Automatic Segmentation of cDNA Microarray Images

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