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https://doi.org/10.1186/1471-2105-11-30
Title: | Enhanced CellClassifier: A multi-class classification tool for microscopy images | Authors: | Misselwitz, B Strittmatter, G Periaswamy, B Schlumberger, M.C Rout, S Horvath, P Kozak, K Hardt, W.-D |
Keywords: | Automated image processing Classification results High-content screening Large-scale perturbations Multi-class classification Open Source Software Programming skills Support vector machine algorithm Automation Classifiers Cytology Image analysis Software engineering Tools Learning systems article automated pattern recognition computer program factual database image processing methodology microscopy Databases, Factual Image Processing, Computer-Assisted Microscopy Pattern Recognition, Automated Software |
Issue Date: | 2010 | Citation: | Misselwitz, B, Strittmatter, G, Periaswamy, B, Schlumberger, M.C, Rout, S, Horvath, P, Kozak, K, Hardt, W.-D (2010). Enhanced CellClassifier: A multi-class classification tool for microscopy images. BMC Bioinformatics 11 : 30. ScholarBank@NUS Repository. https://doi.org/10.1186/1471-2105-11-30 | Rights: | Attribution 4.0 International | Abstract: | Background: Light microscopy is of central importance in cell biology. The recent introduction of automated high content screening has expanded this technology towards automation of experiments and performing large scale perturbation assays. Nevertheless, evaluation of microscopy data continues to be a bottleneck in many projects. Currently, among open source software, CellProfiler and its extension Analyst are widely used in automated image processing. Even though revolutionizing image analysis in current biology, some routine and many advanced tasks are either not supported or require programming skills of the researcher. This represents a significant obstacle in many biology laboratories.Results: We have developed a tool, Enhanced CellClassifier, which circumvents this obstacle. Enhanced CellClassifier starts from images analyzed by CellProfiler, and allows multi-class classification using a Support Vector Machine algorithm. Training of objects can be done by clicking directly "on the microscopy image" in several intuitive training modes. Many routine tasks like out-of focus exclusion and well summary are also supported. Classification results can be integrated with other object measurements including inter-object relationships. This makes a detailed interpretation of the image possible, allowing the differentiation of many complex phenotypes. For the generation of the output, image, well and plate data are dynamically extracted and summarized. The output can be generated as graphs, Excel-files, images with projections of the final analysis and exported as variables.Conclusion: Here we describe Enhanced CellClassifier which allows multiple class classification, elucidating complex phenotypes. Our tool is designed for the biologist who wants both, simple and flexible analysis of images without requiring programming skills. This should facilitate the implementation of automated high-content screening. © 2010 Misselwitz et al; licensee BioMed Central Ltd. | Source Title: | BMC Bioinformatics | URI: | https://scholarbank.nus.edu.sg/handle/10635/181685 | ISSN: | 14712105 | DOI: | 10.1186/1471-2105-11-30 | Rights: | Attribution 4.0 International |
Appears in Collections: | Elements Staff Publications |
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