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Generation of Synthetic Image Datasets for Time-Lapse Fluorescence Microscopy

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SVOBODA David ULMAN Vladimír

Rok publikování 2012
Druh Článek ve sborníku
Konference Proceedings of 9th International Conference on Image Analysis and Recognition
Fakulta / Pracoviště MU

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Citace
Doi http://dx.doi.org/10.1007/978-3-642-31298-4_56
Obor Využití počítačů, robotika a její aplikace
Klíčová slova Simulation; Optical flow; 3D image sequences; Fluorescence optical microscopy
Popis 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.
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