Please use this identifier to cite or link to this item: https://doi.org/10.1186/1471-2105-11-30
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dc.titleEnhanced CellClassifier: A multi-class classification tool for microscopy images
dc.contributor.authorMisselwitz, B
dc.contributor.authorStrittmatter, G
dc.contributor.authorPeriaswamy, B
dc.contributor.authorSchlumberger, M.C
dc.contributor.authorRout, S
dc.contributor.authorHorvath, P
dc.contributor.authorKozak, K
dc.contributor.authorHardt, W.-D
dc.date.accessioned2020-10-27T11:42:01Z
dc.date.available2020-10-27T11:42:01Z
dc.date.issued2010
dc.identifier.citationMisselwitz, 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
dc.identifier.issn14712105
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/181685
dc.description.abstractBackground: 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.
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceUnpaywall 20201031
dc.subjectAutomated image processing
dc.subjectClassification results
dc.subjectHigh-content screening
dc.subjectLarge-scale perturbations
dc.subjectMulti-class classification
dc.subjectOpen Source Software
dc.subjectProgramming skills
dc.subjectSupport vector machine algorithm
dc.subjectAutomation
dc.subjectClassifiers
dc.subjectCytology
dc.subjectImage analysis
dc.subjectSoftware engineering
dc.subjectTools
dc.subjectLearning systems
dc.subjectarticle
dc.subjectautomated pattern recognition
dc.subjectcomputer program
dc.subjectfactual database
dc.subjectimage processing
dc.subjectmethodology
dc.subjectmicroscopy
dc.subjectDatabases, Factual
dc.subjectImage Processing, Computer-Assisted
dc.subjectMicroscopy
dc.subjectPattern Recognition, Automated
dc.subjectSoftware
dc.typeArticle
dc.contributor.departmentMEDICINE
dc.description.doi10.1186/1471-2105-11-30
dc.description.sourcetitleBMC Bioinformatics
dc.description.volume11
dc.description.page30
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