Markov random field modeled range image segmentation
Pattern Recognition Letters
Pixon-based image segmentation with Markov random fields
IEEE Transactions on Image Processing
Gridding spot centers of smoothly distorted microarray images
IEEE Transactions on Image Processing
Quantitative Improvements in cDNA Microarray Spot Segmentation
BSB '09 Proceedings of the 4th Brazilian Symposium on Bioinformatics: Advances in Bioinformatics and Computational Biology
Learning to discover faulty spots in cDNA microarrays
IBERAMIA'10 Proceedings of the 12th Ibero-American conference on Advances in artificial intelligence
Computers in Biology and Medicine
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The segmentation of cDNA microarray spots is essential in analyzing the intensities of microarray images for biological and medical investigation. In this work, nonparametric methods using kernel density estimation are applied to segment two-channel cDNA microarray images. This approach groups pixels into both a foreground and a background. The segmentation performance of this model is tested and evaluated with reference to 16 microarray data. In particular, spike genes with various contents are spotted in a microarray to examine and evaluate the accuracy of the segmentation results. Duplicated design is implemented to evaluate the accuracy of the model. The results of this study demonstrate that this method can cluster pixels and estimate statistics regarding spots with high accuracy.