Department of Computer Science, Northern Illinois University, DeKalb, IL 60115, USA.
BMC Bioinformatics. 2013 Oct 4;14:291. doi: 10.1186/1471-2105-14-291.
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.
模式识别算法在生物图像信息学应用中非常有用，例如量化细胞和亚细胞对象、标注基因表达以及对表型进行分类。为了对不断增加的显微镜图像进行有效且高效的图像分类和标注，我们希望拥有能够组合和比较各种算法并为不同生物问题构建可定制解决方案的工具。然而，当前的工具通常在生成用于标注对应于多个复杂类别的更高维图像的用户友好且可扩展的工具方面提供有限的解决方案。
我们开发了 BIOimage Classification and Annotation Tool（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），以实现自动分类和标注。我们还提出了一种 3D 各向异性小波特征提取器，用于从具有 xy-z 分辨率差异的 3D 图像中提取纹理特征。该提取器是 BIOCAT 中大约 20 个内置特征提取器、选择器和分类器算法之一。这些算法被模块化，以便以可定制的方式“链接”它们，从而为各种问题形成自适应解决方案，基于插件的可扩展性使该工具具有开放架构，可用于合并未来的算法。我们已经将 BIOCAT 应用于具有细胞生物学和神经科学应用的不同属性的图像和 ROI 的分类和标注。
BIOCAT 为基于模式识别的二维和三维图像以及 ROI 的生物图像分类提供了一个用户友好、可移植的平台。我们通过各种案例研究表明，不同的算法及其组合对于各种问题具有不同的适用性。因此，BIOCAT 的可定制性有望为涉及图像分类和标注的各种生物问题提供有效且高效的解决方案。我们还展示了 3D 各向异性小波在分类 3D 图像集和 ROI 方面的有效性。
