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Adaptive filtering for enhancement of the osteocyte cell network in 3D microtomography images
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
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2013 (English)In: IRBM, ISSN 1959-0318, Vol. 34, no 1-SI, 48-52 p.Article in journal (Refereed) Published
Abstract [en]

The osteocyte cell network in bone tissue is thought to orchestrate tissue adaptation and remodeling, thus holding responsibility for tissue quality. Previously, this structure has been studied mainly in 2D and its architecture and functions are not fully elucidated. The assessment of the osteocyte system is prerequisite for deeper understanding of bone remodeling and for advances in management of bone diseases. Our goal is to enable 3D isotropic imaging of bone at cellular level and to develop algorithms for quantitative image analysis of the cell network. We recently demonstrated accurate 3D imaging of this cell structure with synchrotron radiation tomography at submicrometric scale. Due to the limited spatial resolution of the imaging system and the constraints in terms of radiation dose, the images suffer from low signal to noise ratio and the detection of the cell dendrites is challenging. Here we detail a method for enhancement of the osteocyte network in human bone from 3D microtomography images. The approach combines Hessian-based 3D line enhancement and bilateral filtering. Our method enables extraction of the interconnected cells from noisy images, preserving the integrity of the cells and of their slender dendrites. Qualitative and quantitative results are presented. 

Place, publisher, year, edition, pages
2013. Vol. 34, no 1-SI, 48-52 p.
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Natural Sciences
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URN: urn:nbn:se:uu:diva-200125DOI: 10.1016/j.irbm.2012.12.013ISI: 000317530700015OAI: oai:DiVA.org:uu-200125DiVA: diva2:622399
Available from: 2013-05-21 Created: 2013-05-20 Last updated: 2013-05-21Bibliographically approved

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Pacureanu, Alexandra

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