The Essence of Artificial Intelligence
The Essence of Artificial Intelligence
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
A Markov Random Field model of microarray gridding
Proceedings of the 2003 ACM symposium on Applied computing
A Markov Random Field Approach to Microarray Image Gridding
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
A formal analysis of why heuristic functions work
Artificial Intelligence
Computers and Electronics in Agriculture
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
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
Using the Maximum Between-Class Variance for Automatic Gridding of cDNA Microarray Images
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A new method for gridding DNA microarrays
Computers in Biology and Medicine
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Image and statistical analysis are two important stages of cDNA microarrays. Of these, gridding is necessary to accurately identify the location of each spot while extracting spot intensities from the microarray images and automating this procedure permits high-throughput analysis. Due to the deficiencies of the equipment used to print the arrays, rotations, misalignments, high contamination with noise and artifacts, and the enormous amount of data generated, solving the gridding problem by means of an automatic system is not trivial. Existing techniques to solve the automatic grid segmentation problem cover only limited aspects of this challenging problem and require the user to specify the size of the spots, the number of rows and columns in the grid, and boundary conditions. In this paper, a hill-climbing automatic gridding and spot quantification technique is proposed which takes a microarray image (or a subgrid) as input and makes no assumptions about the size of the spots, rows, and columns in the grid. The proposed method is based on a hill-climbing approach that utilizes different objective functions. The method has been found to effectively detect the grids on microarray images drawn from databases from GEO and the Stanford genomic laboratories.