A Markov Random Field model of microarray gridding

Katzer, Mathias; Kummert, Franz; Sagerer, Gerhard

doi:10.1145/952532.952551

Cited by 42 publications

(12 citation statements)

References 9 publications

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“…Such methods are implemented in ScanAlyze [1], ImaGene [2] or SpotFinder [3] that require several parameters to be set by the user. Only a few state of the art methods address the problem of unsupervised gridding based on methods such as mathematical morphology [4], Markov random fields [5], Voronoi diagrams [6,7], Bayesian grid matching [8], Gaussian mixture model [9], genetic algorithms [10] or a combination of approaches [11].…”

Section: Introductionmentioning

confidence: 99%

“…For example, the method proposed in [4] requires that grid rows and columns are strictly aligned with the x and y axes, the region segmentation approach proposed in [5] fails to detect many weak signal spots and in [11] the number of rows and columns of spots per grid is required. The method presented in [8] employs an iterative algorithm to solve a complex deformable model for microarray gridding, but simple linear models such as [10] have been shown to achieve high accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Unsupervised SVM-based gridding for DNA microarray images

Bariamis

Maroulis

Iakovidis

2010

Computerized Medical Imaging and Graphics

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Unsupervised SVM-based gridding for DNA microarray images

Bariamis

Maroulis

Iakovidis

2010

Computerized Medical Imaging and Graphics

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“…Only a few state of the art methods address the problem of automatic gridding. Such methods are based on mathematical morphology [3], Markov random fields [4], Voronoi diagrams [5], Bayesian grid matching [6], genetic algorithms [7] or a combination of approaches [8]. However, there are still problems that have to be resolved before fully automatic gridding can take place.…”

Section: Introductionmentioning

confidence: 99%

“…However, there are still problems that have to be resolved before fully automatic gridding can take place. For example, the method proposed in [3] requires that grid rows and columns are strictly aligned with the x and y axes; the region segmentation approach proposed in [4] fails to detect many weak signal spots; in [8], the number of rows and columns of spots per grid is required; the method proposed in [6] is quite complex; and the genetic approach [7] is very timeconsuming.…”

Section: Introductionmentioning

confidence: 99%

Automatic DNA microarray gridding based on Support Vector Machines

Bariamis

Maroulis

Iakovidis

2008

2008 8th IEEE International Conference on BioInformatics and BioEngineering

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Abstract-This paper presents a novel method for DNA microarray gridding based on Support Vector Machine (SVM) classifiers. It employs a set of soft-margin SVMs to estimate the lines of the DNA microarray grid by maximizing the margin between the lines and the spots. This process comprises an efficient and effective approach of separating the spots into distinct rows and columns. The classifiers are trained using the spot locations as training vectors. The results obtained from the application of the proposed method on reference microarray images illustrate its robustness in the presence of artifacts, noise and weakly expressed spots. The comparative evaluation presented reveals its advantageous performance over a state of the art gridding approach. The gridding quality achieved exceeds 95% in terms of the total number of perfectly gridded spots.

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“…Dapple's techniques are useful for improving the accuracy of data acquisition from DNA microarrays [6] . Mathias, Franz & Gerhard proposed microarray technology and MRF model of microarray gridding that is designed to integrate different application specific constraints and heuristic criteria into a robust and flexible segmentation algorithm [7] . Also there were other researchers who tried to fined deferent ways for gridding for example.…”

Section: Introductionmentioning

confidence: 99%

cDNA Microarray Genome Image Processing Using Fixed Spot Position

Alhadidi¹,

Fakhouri²,

AlMousa³

2006

American J. of Applied Sciences

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Abstract:Complementary DNA (cDNA) microarray is one of the most recent and important technology for exploring the genome. cDNA microarray Image analysis aims to measure the intensity for each spot in the scanned image and this intensity represents the amount of a specific gene in the studied cell. This paper presents a new algorithm that achieves an automated way for applying grid in the cDNA microarray images through the determination of spots position in the cDNA microarray and calculating the mean of every raw and column in the microarray image and the result will be a fast automated system for applying addressing (grid) on the microarray image without any human interaction and this automated system starts from the step that follow the sub microarray image cropping.

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A Markov Random Field model of microarray gridding

Cited by 42 publications

References 9 publications

Unsupervised SVM-based gridding for DNA microarray images

Unsupervised SVM-based gridding for DNA microarray images

Automatic DNA microarray gridding based on Support Vector Machines

cDNA Microarray Genome Image Processing Using Fixed Spot Position

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