ArticleBMC bioinformatics2013
BIOCAT: a pattern recognition platform for customizable biological image classification and annotation.
Article in BMC bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
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- NHL Pathological Image Classification Based on Hierarchical Local Information and GoogLeNet-Based Representations.BioMed research international · 2019Article
- Cell and small animal models for phenotypic drug discovery.Drug design, development and therapy · 2017Review
- Bioimage classification with subcategory discriminant transform of high dimensional visual descriptors.BMC bioinformatics · 2016Article
- Challenges in long-term imaging and quantification of single-cell dynamics.Nature biotechnology · 2016Review
- CP-CHARM: segmentation-free image classification made accessible.BMC bioinformatics · 2016Article
- Discrimination of cell cycle phases in PCNA-immunolabeled cells.BMC bioinformatics · 2015Article
- Learning-guided automatic three dimensional synapse quantification for drosophila neurons.BMC bioinformatics · 2015Article
- An image database of Drosophila melanogaster wings for phenomic and biometric analysis.GigaScience · 2015Article
- A comparative study of cell classifiers for image-based high-throughput screening.BMC bioinformatics · 2014Article
- Understanding health and disease with multidimensional single-cell methods.Journal of physics. Condensed matter : an Institute of Physics journal · 2014Review
- A generic classification-based method for segmentation of nuclei in 3D images of early embryos.BMC bioinformatics · 2014Article
- Knowledge Discovery and interactive Data Mining in Bioinformatics--State-of-the-Art, future challenges and research directions.BMC bioinformatics · 2014Article
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5 authors.
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Abstract
backgroundPattern 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.
resultsWe 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.
conclusionsBIOCAT 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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