| pubmed-article:14643218 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:14643218 | lifeskim:mentions | umls-concept:C0184511 | lld:lifeskim |
| pubmed-article:14643218 | lifeskim:mentions | umls-concept:C0012222 | lld:lifeskim |
| pubmed-article:14643218 | lifeskim:mentions | umls-concept:C0002045 | lld:lifeskim |
| pubmed-article:14643218 | pubmed:issue | 1-2 | lld:pubmed |
| pubmed-article:14643218 | pubmed:dateCreated | 2003-12-3 | lld:pubmed |
| pubmed-article:14643218 | pubmed:abstractText | Cryo-electron tomography is an imaging technique with an unique potential for visualizing large complex biological specimens. It ensures preservation of the biological material but the resulting cryotomograms are extremely noisy. Sophisticated denoising techniques are thus essential for allowing the visualization and interpretation of the information contained in the cryotomograms. Here a software tool based on anisotropic nonlinear diffusion is described for filtering cryotomograms. The approach reduces local noise and meanwhile enhances both curvilinear and planar structures. In the program a novel solution of the partial differential equation has been implemented, which allows a reliable estimation of derivatives and, furthermore, reduces computation time and memory requirements. Several criteria have been included to automatically select the optimal stopping time. The behaviour of the denoising approach is tested for visualizing filamentous structures in cryotomograms. | lld:pubmed |
| pubmed-article:14643218 | pubmed:language | eng | lld:pubmed |
| pubmed-article:14643218 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:14643218 | pubmed:citationSubset | IM | lld:pubmed |
| pubmed-article:14643218 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:14643218 | pubmed:issn | 1047-8477 | lld:pubmed |
| pubmed-article:14643218 | pubmed:author | pubmed-author:TyeBB | lld:pubmed |
| pubmed-article:14643218 | pubmed:author | pubmed-author:FernándezJosé... | lld:pubmed |
| pubmed-article:14643218 | pubmed:issnType | Print | lld:pubmed |
| pubmed-article:14643218 | pubmed:volume | 144 | lld:pubmed |
| pubmed-article:14643218 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:14643218 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:14643218 | pubmed:pagination | 152-61 | lld:pubmed |
| pubmed-article:14643218 | pubmed:dateRevised | 2006-11-15 | lld:pubmed |
| pubmed-article:14643218 | pubmed:meshHeading | pubmed-meshheading:14643218... | lld:pubmed |
| pubmed-article:14643218 | pubmed:meshHeading | pubmed-meshheading:14643218... | lld:pubmed |
| pubmed-article:14643218 | pubmed:meshHeading | pubmed-meshheading:14643218... | lld:pubmed |
| pubmed-article:14643218 | pubmed:meshHeading | pubmed-meshheading:14643218... | lld:pubmed |
| pubmed-article:14643218 | pubmed:meshHeading | pubmed-meshheading:14643218... | lld:pubmed |
| pubmed-article:14643218 | pubmed:meshHeading | pubmed-meshheading:14643218... | lld:pubmed |
| pubmed-article:14643218 | pubmed:meshHeading | pubmed-meshheading:14643218... | lld:pubmed |
| pubmed-article:14643218 | pubmed:articleTitle | An improved algorithm for anisotropic nonlinear diffusion for denoising cryo-tomograms. | lld:pubmed |
| pubmed-article:14643218 | pubmed:affiliation | Department of Computer Architecture and Electronics, University of Almería, 04120 Almería, Spain. jose@ace.ual.es | lld:pubmed |
| pubmed-article:14643218 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:14643218 | pubmed:publicationType | Research Support, Non-U.S. Gov't | lld:pubmed |
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