Abstract
The cDNA microarray technology provides a powerful analytic tool for human genetic research and drug discovery. Image processing plays a crucial role in the extraction and quantitative analysis of the relative abundance of the DNA (Deoxyribonucleic acid) product. The microarray images exhibit variations due to noise impairments. This chapter presents a novel filtering method called Shape-Adaptive DCT (Discrete Cosine Transform), which does well in filtering cDNA microarray images. The experimental result processing by Shape-Adaptive DCT is compared with those obtained from the widely used filtering approaches. Simulation studies reported in this chapter indicate that the proposed filtering method and segmentation using the K-means algorithm yield excellent performance and efficiently suppress noise in cDNA microarray data. The result of the experiment shows its robustness and precision.
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Acknowledgment
This work was supported by the Natural Science Foundation of Jiang Su Province (No. 08KJB510020) and the Natural Science Foundation of Suzhou (SYJG0934).
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Weng, G., Hu, Y., Li, Z. (2012). cDNA Microarray Image Segmentation Using Shape-Adaptive DCT and K-means Clustering. In: Chen, R. (eds) 2011 International Conference in Electrics, Communication and Automatic Control Proceedings. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-8849-2_41
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DOI: https://doi.org/10.1007/978-1-4419-8849-2_41
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