Processing of Microarray Images
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
A deformable grid approach for Bayesian image registration
SPPRA '08 Proceedings of the Fifth IASTED International Conference on Signal Processing, Pattern Recognition and Applications
Sub-grid detection in DNA microarray images
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
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
Biological assessment of grid and spot detection in cDNA microarray images
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Spot addressing for microarray images structured in hexagonal grids
Computer Methods and Programs in 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
A new method for gridding DNA microarrays
Computers in Biology and Medicine
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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