Simulation of fluorescence image formation in 3D light microscopy
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| Year of publication | 2008 |
| Type | Conference abstract |
| MU Faculty or unit | |
| Citation | |
| Description | Fluorescence microscopy still meets the problem of the quality of cell image analysis results. The majority of 2D as well as 3D cell image data acquired using fluorescence microscopy is typically of not very good quality (due to degradations caused by cell preparation, optics and electronics). That is why image processing algorithms applied to this data typically offer imprecise and unreliable results. As the ground truth (GT) for given image data is obviously not available the outputs of different image analysis methods can be neither verified nor compared to each other. In some papers, this problem is partially solved by estimating GT by experts in the field (biologists or physicians). However, in many cases such GT estimate is very subjective and strongly varies among different experts. In order to overcome these difficulties we have created a toolbox that can generate 3D models of artificial biological objects (cells and their components) along with their corresponding images degraded by specific optics and electronics. Image analysis methods can then be applied to such simulated image data. The analysis results (such as segmentation or measurement results) can be compared with GT derived from input models of objects (or measurements on them). In this way, image analysis methods can be compared to each other and their quality (based on difference from GT) can be computed. The present version of the simulation toolbox can generate cells in 3D using deformation of simple shapes and adding texture to the cell interior. Further, it can simulate optical degradations using convolution with supplied point spread function as well as electronic artifacts such as impulse hot pixel noise, additive readout-noise or Poisson photon-shot noise. We have also dealt with the task of evaluating the plausibility of the simulated images in terms of their similarity to real image data. We have tested several similarity criteria such as visual comparison, intensity histograms, central moments, frequency analysis and entropy. The simulation toolbox will be made freely available via simple web interface. |
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