Hybrid clustering for microarray image analysis combining intensity and shape features.
Hybrid clustering for microarray image analysis combining intensity and shape features.
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DOI：
10.1186/1471-2105-5-47
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发表时间：
2004-04-29
影响因子：
3
通讯作者：
Bozinov D
中科院分区：
文献类型：
--
作者：
Rahnenführer J;Bozinov D
图像分析是从微阵列实验中获得可靠结果的第一个关键步骤。首先，必须识别图像中属于单个斑点的区域。然后，这些目标区域必须被划分为前景和背景。最后，必须提取强度的两个标量值。这些目标已经通过光斑形状方法或强度直方图方法来解决，但是期望具有联合收割机，其结合了两种方法的优点。一种新的鲁棒的和自适应的直方图类型的方法是像素聚类，它已成功地应用于检测和定量微阵列斑点。本文演示了如何斑点形状可以有效地集成在这种方法。基于聚类结果，构造了二价掩模。它估计预期的光斑形状，并用于过滤数据，改善聚类算法的结果。定义质量度量“稳定性”并在真实的数据集上进行评估。将改进的聚类方法与已有的Spot软件在一个重复数据集上进行了比较。新方法提出了一种成功的混合微阵列图像分析解决方案。它结合了形状和直方图特征，并特别适用于处理典型的微阵列图像特征。作为滤波步骤的结果，像素被分成三组，即前景、背景和删除。这允许对伪影进行单独处理并从进一步分析中消除伪影。
Image analysis is the first crucial step to obtain reliable results from microarray experiments. First, areas in the image belonging to single spots have to be identified. Then, those target areas have to be partitioned into foreground and background. Finally, two scalar values for the intensities have to be extracted. These goals have been tackled either by spot shape methods or intensity histogram methods, but it would be desirable to have hybrid algorithms which combine the advantages of both approaches. A new robust and adaptive histogram type method is pixel clustering, which has been successfully applied for detecting and quantifying microarray spots. This paper demonstrates how the spot shape can be effectively integrated in this approach. Based on the clustering results, a bivalence mask is constructed. It estimates the expected spot shape and is used to filter the data, improving the results of the cluster algorithm. The quality measure 'stability' is defined and evaluated on a real data set. The improved clustering method is compared with the established Spot software on a data set with replicates. The new method presents a successful hybrid microarray image analysis solution. It incorporates both shape and histogram features and is specifically adapted to deal with typical microarray image characteristics. As a consequence of the filtering step pixels are divided into three groups, namely foreground, background and deletions. This allows a separate treatment of artifacts and their elimination from the further analysis.
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DOI：
10.1198/106186002317375640
发表时间：
2002-03-01
影响因子：
2.4
作者：
Yang, YH;Buckley, MJ;Speed, TP
通讯作者：
Speed, TP
DOI：
10.1016/s0734-189x(85)90153-7
发表时间：
1985-01-01
期刊：
COMPUTER VISION GRAPHICS AND IMAGE PROCESSING
影响因子：
--
作者：
HARALICK, RM;SHAPIRO, LG
通讯作者：
SHAPIRO, LG
影响因子：
1.8
作者：
Gordon, AD
通讯作者：
Gordon, AD
影响因子：
3.5
作者：
Chen, Y;Dougherty, E R;Bittner, M L
通讯作者：
Bittner, M L
影响因子：
56.9
作者：
SCHENA, M;SHALON, D;BROWN, PO
通讯作者：
BROWN, PO