Localizing and extracting filament distributions from microscopy images.
Identifieur interne : 000439 ( Main/Exploration ); précédent : 000438; suivant : 000440Localizing and extracting filament distributions from microscopy images.
Auteurs : S. Basu [États-Unis] ; C. Liu ; G K RohdeSource :
- Journal of microscopy [ 1365-2818 ] ; 2015.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- methods : Microscopy, Microscopy, Atomic Force, Microscopy, Confocal.
- ultrastructure : Actin Cytoskeleton, Cytoskeleton.
- Algorithms, Diffusion.
Abstract
Detailed quantitative measurements of biological filament networks represent a crucial step in understanding architecture and structure of cells and tissues, which in turn explain important biological events such as wound healing and cancer metastases. Microscopic images of biological specimens marked for different structural proteins constitute an important source for observing and measuring meaningful parameters of biological networks. Unfortunately, current efforts at quantitative estimation of architecture and orientation of biological filament networks from microscopy images are predominantly limited to visual estimation and indirect experimental inference. Here, we describe a new method for localizing and extracting filament distributions from 2D microscopy images of different modalities. The method combines a filter-based detection of pixels likely to contain a filament with a constrained reverse diffusion-based approach for localizing the filaments centrelines. We show with qualitative and quantitative experiments, using both simulated and real data, that the new method can provide more accurate centreline estimates of filament in comparison to other approaches currently available. In addition, we show the algorithm is more robust with respect to variations in the initial filter-based filament detection step often used. We demonstrate the application of the method in extracting quantitative parameters from confocal microscopy images of actin filaments and atomic force microscopy images of DNA fragments.
DOI: 10.1111/jmi.12209
PubMed: 25556529
Affiliations:
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Basu</name><affiliation wicri:level="4"><nlm:affiliation>Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, Pennsylvania, U.S.A.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, Pennsylvania</wicri:regionArea><placeName><region type="state">Pennsylvanie</region><settlement type="city">Pittsburgh</settlement></placeName><orgName type="university">Université Carnegie-Mellon</orgName></affiliation></author><author><name sortKey="Liu, C" sort="Liu, C" uniqKey="Liu C" first="C" last="Liu">C. Liu</name></author><author><name sortKey="Rohde, G K" sort="Rohde, G K" uniqKey="Rohde G" first="G K" last="Rohde">G K Rohde</name></author></analytic><series><title level="j">Journal of microscopy</title><idno type="eISSN">1365-2818</idno><imprint><date when="2015" type="published">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Actin Cytoskeleton (ultrastructure)</term><term>Algorithms</term><term>Cytoskeleton (ultrastructure)</term><term>Diffusion</term><term>Microscopy (methods)</term><term>Microscopy, Atomic Force (methods)</term><term>Microscopy, Confocal (methods)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes</term><term>Cytosquelette (ultrastructure)</term><term>Cytosquelette d'actine (ultrastructure)</term><term>Diffusion</term><term>Microscopie ()</term><term>Microscopie confocale ()</term><term>Microscopie à force atomique ()</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Microscopy</term><term>Microscopy, Atomic Force</term><term>Microscopy, Confocal</term></keywords><keywords scheme="MESH" qualifier="ultrastructure" xml:lang="en"><term>Actin Cytoskeleton</term><term>Cytoskeleton</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Diffusion</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Cytosquelette</term><term>Cytosquelette d'actine</term><term>Diffusion</term><term>Microscopie</term><term>Microscopie confocale</term><term>Microscopie à force atomique</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Detailed quantitative measurements of biological filament networks represent a crucial step in understanding architecture and structure of cells and tissues, which in turn explain important biological events such as wound healing and cancer metastases. Microscopic images of biological specimens marked for different structural proteins constitute an important source for observing and measuring meaningful parameters of biological networks. Unfortunately, current efforts at quantitative estimation of architecture and orientation of biological filament networks from microscopy images are predominantly limited to visual estimation and indirect experimental inference. Here, we describe a new method for localizing and extracting filament distributions from 2D microscopy images of different modalities. The method combines a filter-based detection of pixels likely to contain a filament with a constrained reverse diffusion-based approach for localizing the filaments centrelines. We show with qualitative and quantitative experiments, using both simulated and real data, that the new method can provide more accurate centreline estimates of filament in comparison to other approaches currently available. In addition, we show the algorithm is more robust with respect to variations in the initial filter-based filament detection step often used. We demonstrate the application of the method in extracting quantitative parameters from confocal microscopy images of actin filaments and atomic force microscopy images of DNA fragments.</div></front></TEI><affiliations><list><country><li>États-Unis</li></country><region><li>Pennsylvanie</li></region><settlement><li>Pittsburgh</li></settlement><orgName><li>Université Carnegie-Mellon</li></orgName></list><tree><noCountry><name sortKey="Liu, C" sort="Liu, C" uniqKey="Liu C" first="C" last="Liu">C. Liu</name><name sortKey="Rohde, G K" sort="Rohde, G K" uniqKey="Rohde G" first="G K" last="Rohde">G K Rohde</name></noCountry><country name="États-Unis"><region name="Pennsylvanie"><name sortKey="Basu, S" sort="Basu, S" uniqKey="Basu S" first="S" last="Basu">S. 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