Please use this identifier to cite or link to this item: 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

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
10_1186_1471-2105-11-30.pdf4 MBAdobe PDF

OPEN

NoneView/Download

SCOPUSTM   
Citations

67
checked on Jan 12, 2021

Page view(s)

19
checked on Jan 14, 2021

Google ScholarTM

Check

Altmetric


This item is licensed under a Creative Commons License Creative Commons