Please use this identifier to cite or link to this item:
|Title:||Learning cell geometry models for cell image simulation: An unbiased approach|
|Source:||Xiong, W., Wang, Y., Ong, S.H., Lim, J.H., Jiang, L. (2010). Learning cell geometry models for cell image simulation: An unbiased approach. Proceedings - International Conference on Image Processing, ICIP : 1897-1900. ScholarBank@NUS Repository. https://doi.org/10.1109/ICIP.2010.5652455|
|Abstract:||Computer generation of cell images can provide annotated data to simulate various imaging conditions with controllable parameters. Synthesized images based on simple models cannot reflect the complicated parameter constraints in simulating real objects in terms of their deformation with appropriate probabilities. Learning-based techniques can provide insight to these properties and impose constraints on deformation selections. In this work, we discuss the simulation of gray level images of healthy red blood cell populations. Different from existing techniques, we learn the unbiased average shape and deformation models of the cells. Both models are used to guide the selection of possible deformations. We also learn cell color models to govern the texture generation of simulated cells. We apply this technique to simulate cell populations and validate the results using cell segmentation and counting algorithms. The proposed learning and simulation technique is generic and can be applied to other types of cells as well. © 2010 IEEE.|
|Source Title:||Proceedings - International Conference on Image Processing, ICIP|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Mar 7, 2018
WEB OF SCIENCETM
checked on Jan 29, 2018
checked on Mar 11, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.