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Segmentation of cDNA Microarray Image using Fuzzy c-mean Algorithm and Mathematical Morphology

机译:基于模糊c-均值算法和数学形态学的cDNA微阵列图像分割

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cDNA microarray technology provides an effectual tool to explore the enormous genome. cDNA microarray consists of thousands of gene sequences which are printed on glass slide and these sequence information can be obtained by forming a microarray image. So image analysis is crucial. However, image segmentation is another key point. How to deal with the gene spots which are always comprised with imperfection such as irregular contours, donut shapes, artifact and spots with low expression is important to the robustness of the segmentation method. The paper proposed a method based on fuzzy c-mean algorithm which can effectively avoid the influence of various types of artifacts through adaptive partitioning.
机译:cDNA微阵列技术提供了探索巨大基因组的有效工具。 cDNA微阵列由数以千计的基因序列组成,这些基因序列印在载玻片上,这些序列信息可以通过形成微阵列图像来获得。因此,图像分析至关重要。但是,图像分割是另一个关键点。如何处理总是有缺陷的基因斑点,例如不规则的轮廓,甜甜圈形状,伪影和低表达的斑点,对于分割方法的鲁棒性至关重要。提出了一种基于模糊c均值算法的方法,该方法可以通过自适应划分有效避免各种伪像的影响。

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