A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
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
Modeling nonlinearity in dilution design microarray data
Bioinformatics
Pattern Recognition, Fourth Edition
Pattern Recognition, Fourth Edition
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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One of the main issues of the analysis of microarray data is quantification of gene expression. The quantified signal intensities should be linearly related to the expression levels of the corresponding genes. In this paper, we present a biological assessment for detection and segmentation of grids and spots, and quantification of gene expression in cDNA microarray images. The results on several dilution steps on cDNA microarray images show that the proposed method can detect the location of the spots very effectively even for noisy conditions based on a parameterless multilevel thresholding algorithm. The proposed method can also segment and quantify the intensity of each probe with a nearly perfect degree of accuracy. This guarantees that the proposed method estimates the correct intensity of each spot with a high degree of accuracy and relates it to the expression levels of the corresponding genes very well.