一种基于聚类和统计分析DNA基因芯片图像处理算法

DNA基因芯片可以同时监控成千上万个基因的表达信息。图像分析是基因芯片试验中一个重要的环节,直接影响到其后续的处理、分析和研究,比如鉴别预测具有不同表达信息的基因功能。基因芯片图像分析包括三个步骤:图像网格化,图像分割以及信息抽取。该文主要研究分割和信息抽取问题。首先基于K-Means聚类技术提出了一种新的分割方法;其次基于统计分析文章建议了一种新的背景和前景分割校正方法用于更准确的信息抽取。新方法的优点是对于基因芯片中spot图像没有任何形状限制。实际图像分析结果与目前最流行的基因芯片图像分析软件GenePix对比研究表明该文算法是精确有效的。

DNA Microarray Image Analysis Based on Clustering and Statistic Analysis

DNA microarray allows the monitoring of expressions for tens of thousands of genes simultaneously.Image analysis is an important aspect for microarray experiments that can affect subsequent analysis such as identification of differentially expressed genes.Image processing for microarray images includes three tasks:spot gridding,segmentation and information extraction.In this paper,we address the segmentation and information extraction problems ,and propose a new segmentation method based on K-Means clustering and a new background and foreground segmentation correction method for accurate information extraction.The advantage of our method is that there is no any restriction on the spot shape in DNA microarray.We compare our experimental results with those obtained from the widely used software GenePix.