A multiresolution approach to automated classification of protein subcellular location images.
A multiresolution approach to automated classification of protein subcellular location images.
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DOI：
10.1186/1471-2105-8-210
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发表时间：
2007-06-19
影响因子：
3
通讯作者：
Kovacević J
中科院分区：
文献类型：
--
作者：
Chebira A;Barbotin Y;Jackson C;Merryman T;Srinivasa G;Murphy RF;Kovacević J
荧光显微镜被广泛用于确定蛋白质的亚细胞位置。在蛋白质组的基础上确定位置的努力产生了对分析结果图像的自动化方法的需求。在过去的十年里，使用不同的特征集和分类器，使用机器学习方法识别所有主要的亚细胞定位模式的可行性已经被令人信服地证明了。在一个经过充分研究的二维HeLa单细胞图像数据集上，通过包含一组多分辨率特征，获得了迄今为止最好的性能，91.5%。这表明了多分辨率方法对这一重要问题的价值。我们在这里报告了一种新的方法，通过在多分辨率子空间中分类来分类亚细胞定位模式。我们的系统能够与任何特征集和任何分类器一起工作。它包括多分辨率(MR)分解，然后在每个MR子空间中进行特征计算和分类，产生局部决策，然后将这些决策组合成全局决策。单独使用26个纹理特征和一个神经网络分类器，我们在二维HeLa数据集上的准确率提高到95.3%。我们证明了多分辨率空间中的空频局域信息显著增加了系统的分辨能力。此外，我们表明，由我们的新的改进的Haralick纹理特征组成的大大减少的特征集是足够的。我们提出的系统是通用的，允许特征集的任意组合和分类器的任意组合。
Fluorescence microscopy is widely used to determine the subcellular location of proteins. Efforts to determine location on a proteome-wide basis create a need for automated methods to analyze the resulting images. Over the past ten years, the feasibility of using machine learning methods to recognize all major subcellular location patterns has been convincingly demonstrated, using diverse feature sets and classifiers. On a well-studied data set of 2D HeLa single-cell images, the best performance to date, 91.5%, was obtained by including a set of multiresolution features. This demonstrates the value of multiresolution approaches to this important problem. We report here a novel approach for the classification of subcellular location patterns by classifying in multiresolution subspaces. Our system is able to work with any feature set and any classifier. It consists of multiresolution (MR) decomposition, followed by feature computation and classification in each MR subspace, yielding local decisions that are then combined into a global decision. With 26 texture features alone and a neural network classifier, we obtained an increase in accuracy on the 2D HeLa data set to 95.3%. We demonstrate that the space-frequency localized information in the multiresolution subspaces adds significantly to the discriminative power of the system. Moreover, we show that a vastly reduced set of features is sufficient, consisting of our novel modified Haralick texture features. Our proposed system is general, allowing for any combinations of sets of features and any combination of classifiers.
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影响因子：
20.6
作者：
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