Performance Evaluation of Some Clustering Algorithms and Validity Indices
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Markov Random Field model of microarray gridding
Proceedings of the 2003 ACM symposium on Applied computing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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 Hill-Climbing Approach for Automatic Gridding of cDNA Microarray Images
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Recognition of perspectively distorted planar grids
Pattern Recognition Letters
Information Sciences: an International Journal
An Efficient Algorithm for Optimal Multilevel Thresholding of Irregularly Sampled Histograms
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Sub-grid detection in DNA microarray images
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
A Deformable Grid-Matching Approach for Microarray Images
IEEE Transactions on Image Processing
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The analysis of DNA microarray images is a crucial step in gene expression analysis, since any errors in early stages are propagated in future steps in the analysis. When processing the underlying images, accurately separating the sub-grids and spots is of extreme importance for subsequent steps that include segmentation, quantification, normalization and clustering. We propose a fully automatic approach that first detects the sub-grids given the entire microarray image, and then detects the locations of the spots in each sub-grid. The approach first detects and corrects rotations in the images by an affine transformation, followed by a polynomial-time optimal multi-level thresholding algorithm to find the positions of the sub-grids and spots. Additionally, a new validity index is proposed in order to find the correct number of sub-grids in the microarray image, and the correct number of spots in each sub-grid. Extensive experiments on real-life microarray images show that the method performs these tasks automatically and with a high degree of accuracy.