Visual reconstruction
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Computer Vision
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International Journal of Systems, Control and Communications
Gridding and Compression of Microarray Images
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
A Hill-Climbing Approach for Automatic Gridding of cDNA Microarray Images
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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BioMed'06 Proceedings of the 24th IASTED international conference on Biomedical engineering
Microarray image gridding with stochastic search based approaches
Image and Vision Computing
Sub-grid detection in DNA microarray images
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
Constructing the histogram representation for automatic gridding of cDNA microarray images
ICMB'08 Proceedings of the 1st international conference on Medical biometrics
Sub-grid and spot detection in DNA microarray images using optimal multi-level thresholding
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
A new approach to automatically detecting grids in DNA microarray images
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Biological assessment of grid and spot detection in cDNA microarray images
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Applications of multilevel thresholding algorithms to transcriptomics data
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
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DNA microarray hybridisation is a popular high through-put technique in academic as well as industrial functional genomics research. In this paper we present a new approach to automatic grid segmentation of the raw fluorescence microarray images by Markov Random Field (MRF) techniques. The main objectives are applicability to various types of array designs and robustness to the typical problems encountered in microarray images, which are contaminations and weak signal.We briefly introduce microarray technology and give some background on MRFs. Our MRF model of microarray gridding is designed to integrate different application specific constraints and heuristic criteria into a robust and flexible segmentation algorithm. We show how to compute the model components efficiently and state our deterministic MRF energy minimization algorithm that was derived from the 'Highest Confidence First' algorithm by Chou et al. Since MRF segmentation may fail due to the properties of the data and the minimization algorithm, we use supplied or estimated print layouts to validate results.Finally we present results of tests on several series of microarray images from different sources, some of them test sets published with other microarray gridding software. Our MRF grid segmentation requires weaker assumptions about the array printing process than previously published methods and produces excellent results on many real datasets.An implementation of the described methods is available upon request from the authors.