Name
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICIP.2010.5652455 | ICIP |
Keywords | Field | DocType |
healthy red blood cell populations,cellular biophysics,synthesized images,simulated cells,cell segmentation,computer generation,deformation selections,annotated data,learning (artificial intelligence),cell image simulation,image segmentation,learning-based techniques,cell images,cell,deformation probability,simulation,deformation,learning cell geometry models,unbiased average shape,texture generation,cell color models,simulation technique,counting algorithms,deformation models,imaging conditions,unbiased model,parameter constraints,learning,medical image processing,controllable parameters,gray level images,image colour analysis,probability,computational modeling,learning artificial intelligence,color model,shape,data models | Computer vision,Data modeling,Cellular biophysics,Pattern recognition,Computer science,Image segmentation,Artificial intelligence,Gray level,Color model,Cell segmentation | Conference |
ISSN | ISBN | Citations |
1522-4880 E-ISBN : 978-1-4244-7993-1 | 978-1-4244-7993-1 | 5 |
PageRank | References | Authors |
0.56 | 4 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Xiong | 1 | 23 | 6.75 |
| Yanbo Wang | 2 | 88 | 14.39 |
| S. H. Ong | 3 | 570 | 46.58 |
| Joo-Hwee Lim | 4 | 783 | 82.45 |
| Lijun Jiang | 5 | 5 | 0.56 |