BIOCAT: a pattern recognition platform for customizable biological image classification and annotation.
BIOCAT: a pattern recognition platform for customizable biological image classification and annotation.
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DOI：
10.1186/1471-2105-14-291
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发表时间：
2013-10-04
影响因子：
3
通讯作者：
Peng H
中科院分区：
文献类型：
--
作者：
Zhou J;Lamichhane S;Sterne G;Ye B;Peng H
模式识别算法在生物图像信息学应用中是有用的，例如量化细胞和亚细胞对象、注释基因表达和分类表型。为了对不断增加的显微图像提供有效且高效的图像分类和注释，期望具有能够联合收割机和比较各种算法的工具，并且针对不同的生物问题构建可定制的解决方案。然而，目前的工具往往提供了一个有限的解决方案，在生成用户友好的和可扩展的工具，用于注释高维图像，对应于多个复杂的类别。我们开发了生物图像分类和注释工具（BIOCAT）。它能够将模式识别算法应用于二维和三维生物图像集以及单个图像中的感兴趣区域（ROI），以进行自动分类和注释。我们还提出了一个三维各向异性小波特征提取器，用于从具有xy-z分辨率视差的三维图像中提取纹理特征。该提取器是BIOCAT中大约20种内置的特征提取器、选择器和分类器算法之一。该算法是模块化的，使他们可以“链”在一个可定制的方式，形成适应性的解决方案，为各种问题，和基于插件的可扩展性，使该工具的开放式架构，以纳入未来的算法。我们已经将BIOCAT应用于细胞生物学和神经科学中不同属性的图像和ROI的分类和注释。BIOCAT提供了一个用户友好的便携式平台，用于基于模式识别的二维和三维图像和ROI的生物图像分类。我们表明，通过不同的案例研究，不同的算法和它们的组合有不同的适合各种问题。因此，BIOCAT的可定制性有望为涉及图像分类和注释的各种生物学问题提供有效和高效的解决方案。我们还证明了三维各向异性小波在分类三维图像集和ROI的有效性。
Pattern recognition algorithms are useful in bioimage informatics applications such as quantifying cellular and subcellular objects, annotating gene expressions, and classifying phenotypes. To provide effective and efficient image classification and annotation for the ever-increasing microscopic images, it is desirable to have tools that can combine and compare various algorithms, and build customizable solution for different biological problems. However, current tools often offer a limited solution in generating user-friendly and extensible tools for annotating higher dimensional images that correspond to multiple complicated categories. We develop the BIOimage Classification and Annotation Tool (BIOCAT). It is able to apply pattern recognition algorithms to two- and three-dimensional biological image sets as well as regions of interest (ROIs) in individual images for automatic classification and annotation. We also propose a 3D anisotropic wavelet feature extractor for extracting textural features from 3D images with xy-z resolution disparity. The extractor is one of the about 20 built-in algorithms of feature extractors, selectors and classifiers in BIOCAT. The algorithms are modularized so that they can be “chained” in a customizable way to form adaptive solution for various problems, and the plugin-based extensibility gives the tool an open architecture to incorporate future algorithms. We have applied BIOCAT to classification and annotation of images and ROIs of different properties with applications in cell biology and neuroscience. BIOCAT provides a user-friendly, portable platform for pattern recognition based biological image classification of two- and three- dimensional images and ROIs. We show, via diverse case studies, that different algorithms and their combinations have different suitability for various problems. The customizability of BIOCAT is thus expected to be useful for providing effective and efficient solutions for a variety of biological problems involving image classification and annotation. We also demonstrate the effectiveness of 3D anisotropic wavelet in classifying both 3D image sets and ROIs.
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DOI：
10.1083/jcb.201004104
发表时间：
2010-05-31
期刊：
The Journal of cell biology
影响因子：
--
作者：
Linkert M;Rueden CT;Allan C;Burel JM;Moore W;Patterson A;Loranger B;Moore J;Neves C;Macdonald D;Tarkowska A;Sticco C;Hill E;Rossner M;Eliceiri KW;Swedlow JR
通讯作者：
Swedlow JR
影响因子：
46.9
作者：
通讯作者：
--
影响因子：
12.3
作者：
Carpenter AE;Jones TR;Lamprecht MR;Clarke C;Kang IH;Friman O;Guertin DA;Chang JH;Lindquist RA;Moffat J;Golland P;Sabatini DM
通讯作者：
Sabatini DM
DOI：
10.1109/tit.1962.1057692
发表时间：
1962-01-01
期刊：
IRE TRANSACTIONS ON INFORMATION THEORY
影响因子：
--
作者：
HU, M
通讯作者：
HU, M
影响因子：
48
作者：
de Chaumont, Fabrice;Dallongeville, Stephane;Olivo-Marin, Jean-Christophe
通讯作者：
Olivo-Marin, Jean-Christophe