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A simple segmentation method for DNA microarray spots by kernel density estimation

机译:通过核密度估计的DNA芯片斑点的简单分割方法

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The DNA microarray analysis is one of the most important areas in biomedical research. For the accurate analysis of microarray data the process of segmentation, classification of pixels as foreground or background, should be done accurately. In this paper we suggest a kernel density estimation approach for the segmentation of the microarray spot. We estimate the density of n pixel intensities for a given target area by the kernel density estimation, and the resulting kernel density estimate gives bimodal density by appropriate choice of the smoothing parameter. We suggest two modes of the kernel density estimate for n pixel intensities as estimates of the foreground (mode with larger value) and the background (mode with smaller value) intensity, respectively. The segmentation method proposed in this paper is easy and simple to use, robust to the shape of spot, and very accurate.
机译:DNA芯片分析是生物医学研究中最重要的领域之一。为了精确分析微阵列数据,应该准确地进行分割,将像素分类为前景或背景的过程。在本文中,我们建议使用核密度估计方法对微阵列斑点进行分割。我们通过核密度估计来估计给定目标区域的n个像素强度的密度,并且通过适当选择平滑参数,所得核密度估计会给出双峰密度。我们建议对n个像素强度进行内核密度估计的两种模式,分别是对前景(具有较大值的模式)和背景(具有较小值的模式)强度的估计。本文提出的分割方法简单易用,对斑点形状具有鲁棒性,并且非常准确。

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