The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
Simulation toolbox for 3D-FISH Spot-counting algorithms
Real-Time Imaging
Cardiac Motion Simulator for Tagged MRI
MMBIA '96 Proceedings of the 1996 Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA '96)
Pseudo-real image sequence generator for optical flow computations
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
On simulating 3D fluorescent microscope images
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Papsynth: simulated bright-field images of cervical smears
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Optimal-flow minimum-cost correspondence assignment in particle flow tracking
Computer Vision and Image Understanding
Generation of 3D digital phantoms of colon tissue
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part II
Segmenting and tracking fluorescent cells in dynamic 3-D microscopy with coupled active surfaces
IEEE Transactions on Image Processing
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In the field of biomedical image analysis, motion tracking and segmentation algorithms are important tools for time-resolved analysis of cell characteristics, events, and tracking. There are many algorithms in everyday use. Nevertheless, most of them is not properly validated as the ground truth (GT), which is a very important tool for the verification of image processing algorithms, is not naturally available. Many algorithms in this field of study are, therefore, validated only manually by an human expert. This is usually difficult, cumbersome and time consuming task, especially when single 3D image or even 3D image sequence is considered. In this paper, we have proposed a technique that generates time-lapse sequences of fully 3D synthetic image datasets. It includes generating shape, structure, and also motion of selected biological objects. The corresponding GT data is generated as well. The technique is focused on the generation of synthetic objects at various scales. Such datasets can be then processed by selected segmentation or motion tracking algorithms. The results can be compared with the GT and the quality of the applied algorithm can be measured.