2007
DOI: 10.1109/tmi.2007.896925
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Computational Framework for Simulating Fluorescence Microscope Images With Cell Populations

Abstract: Fluorescence microscopy combined with digital imaging constructs a basic platform for numerous biomedical studies in the field of cellular imaging. As the studies relying on analysis of digital images have become popular, the validation of image processing methods used in automated image cytometry has become an important topic. Especially, the need for efficient validation has arisen from emerging high-throughput microscopy systems where manual validation is impractical. We present a simulation platform for ge… Show more

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Cited by 184 publications
(174 citation statements)
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“…The bacterial cells dataset: This dataset contains 200 synthetic images, emulating microscopic views of the colonies of bacterial cell [18]. Such synthetic images are highly realistic and simulate effects such as cell overlaps, shape variability, out-of-focus blur, vignetting, etc.…”
Section: A Wait Time and Queue Estimationmentioning
confidence: 99%
“…The bacterial cells dataset: This dataset contains 200 synthetic images, emulating microscopic views of the colonies of bacterial cell [18]. Such synthetic images are highly realistic and simulate effects such as cell overlaps, shape variability, out-of-focus blur, vignetting, etc.…”
Section: A Wait Time and Queue Estimationmentioning
confidence: 99%
“…The experimental dataset is produced with a simulation program [18]. It is very unbalanced due to scarce positive examples.…”
Section: Cell Images (Binary Classification)mentioning
confidence: 99%
“…Analysis on cell and organelle shapes can be done in a manner similar to nuclear shape. A vast majority of methods use parametric representation of cell shapes [31,32,33].…”
Section: Shape Analysismentioning
confidence: 99%