High content image analysis for human H4 neuroglioma cells exposed to CuO nanoparticles
Open Access
- 9 October 2007
- journal article
- Published by Springer Nature in BMC Biotechnology
- Vol. 7 (1) , 66
Abstract
Background: High content screening (HCS)-based image analysis is becoming an important and widely used research tool. Capitalizing this technology, ample cellular information can be extracted from the high content cellular images. In this study, an automated, reliable and quantitative cellular image analysis system developed in house has been employed to quantify the toxic responses of human H4 neuroglioma cells exposed to metal oxide nanoparticles. This system has been proved to be an essential tool in our study. Results: The cellular images of H4 neuroglioma cells exposed to different concentrations of CuO nanoparticles were sampled using IN Cell Analyzer 1000. A fully automated cellular image analysis system has been developed to perform the image analysis for cell viability. A multiple adaptive thresholding method was used to classify the pixels of the nuclei image into three classes: bright nuclei, dark nuclei, and background. During the development of our image analysis methodology, we have achieved the followings: (1) The Gaussian filtering with proper scale has been applied to the cellular images for generation of a local intensity maximum inside each nucleus; (2) a novel local intensity maxima detection method based on the gradient vector field has been established; and (3) a statistical model based splitting method was proposed to overcome the under segmentation problem. Computational results indicate that 95.9% nuclei can be detected and segmented correctly by the proposed image analysis system. Conclusion: The proposed automated image analysis system can effectively segment the images of human H4 neuroglioma cells exposed to CuO nanoparticles. The computational results confirmed our biological finding that human H4 neuroglioma cells had a dose-dependent toxic response to the insult of CuO nanoparticles.Keywords
This publication has 16 references indexed in Scilit:
- Automated Segmentation, Classification, and Tracking of Cancer Cell Nuclei in Time-Lapse MicroscopyIEEE Transactions on Biomedical Engineering, 2006
- Hierarchical, model‐based merging of multiple fragments for improved three‐dimensional segmentation of nucleiCytometry Part A, 2004
- Multidimensional Drug Profiling By Automated MicroscopyScience, 2004
- Image analysis for automatic segmentation of cytoplasms and classification of Rac1 activationCytometry Part A, 2003
- A hybrid 3D watershed algorithm incorporating gradient cues and object models for automatic segmentation of nuclei in confocal image stacksCytometry Part A, 2003
- Floating search methods in feature selectionPattern Recognition Letters, 1994
- Watersheds in digital spaces: an efficient algorithm based on immersion simulationsIEEE Transactions on Pattern Analysis and Machine Intelligence, 1991
- Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulationsJournal of Computational Physics, 1988
- Comparative in vitro cytotoxicity of nickel oxides and nickel‐copper oxides to rat, mouse, and dog pulmonary alveolar macrophagesJournal of Toxicology and Environmental Health, 1988
- A segmentation system based on thresholdingComputer Graphics and Image Processing, 1981