A bioimage informatics approach to automatically extract complex fungal networks.
Identifieur interne : 000417 ( Main/Curation ); précédent : 000416; suivant : 000418A bioimage informatics approach to automatically extract complex fungal networks.
Auteurs : Boguslaw Obara [Royaume-Uni] ; Vicente Grau ; Mark D. FrickerSource :
- Bioinformatics (Oxford, England) [ 1367-4811 ] ; 2012.
Descripteurs français
- KwdFr :
- MESH :
- cytologie : Champignons, Mycelium, Phanerochaete.
- méthodes : Traitement d'image par ordinateur.
- Algorithmes.
English descriptors
- KwdEn :
- MESH :
- cytology : Fungi, Mycelium, Phanerochaete.
- methods : Image Processing, Computer-Assisted.
- Algorithms.
Abstract
MOTIVATION
Fungi form extensive interconnected mycelial networks that scavenge efficiently for scarce resources in a heterogeneous environment. The architecture of the network is highly responsive to local nutritional cues, damage or predation, and continuously adapts through growth, branching, fusion or regression. These networks also provide an example of an experimental planar network system that can be subjected to both theoretical analysis and experimental manipulation in multiple replicates. For high-throughput measurements, with hundreds of thousands of branches on each image, manual detection is not a realistic option, especially if extended time series are captured. Furthermore, branches typically show considerable variation in contrast as the individual cords span several orders of magnitude and the compressed soil substrate is not homogeneous in texture making automated segmentation challenging.
RESULTS
We have developed and evaluated a high-throughput automated image analysis and processing approach using Phase Congruency Tensors and watershed segmentation to characterize complex fungal networks. The performance of the proposed approach is evaluated using complex images of saprotrophic fungal networks with 10(5)-10(6) edges. The results obtained demonstrate that this approach provides a fast and robust solution for detection and graph-based representation of complex curvilinear networks.
AVAILABILITY AND IMPLEMENTATION
The Matlab toolbox is freely available through the Oxford e-Research Centre website: http://www.oerc.ox.ac.uk/research/bioimage/software
CONTACTS
boguslaw.obara@oerc.ox.ac.uk.
DOI: 10.1093/bioinformatics/bts364
PubMed: 22743223
Links toward previous steps (curation, corpus...)
- to stream Main, to step Corpus: Pour aller vers cette notice dans l'étape Curation :000417
Links to Exploration step
pubmed:22743223Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">A bioimage informatics approach to automatically extract complex fungal networks.</title><author><name sortKey="Obara, Boguslaw" sort="Obara, Boguslaw" uniqKey="Obara B" first="Boguslaw" last="Obara">Boguslaw Obara</name><affiliation wicri:level="1"><nlm:affiliation>Oxford e-Research Centre, University of Oxford, UK. boguslaw.obara@oerc.ox.ac.uk</nlm:affiliation><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>Oxford e-Research Centre, University of Oxford</wicri:regionArea></affiliation></author><author><name sortKey="Grau, Vicente" sort="Grau, Vicente" uniqKey="Grau V" first="Vicente" last="Grau">Vicente Grau</name></author><author><name sortKey="Fricker, Mark D" sort="Fricker, Mark D" uniqKey="Fricker M" first="Mark D" last="Fricker">Mark D. Fricker</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2012">2012</date><idno type="RBID">pubmed:22743223</idno><idno type="pmid">22743223</idno><idno type="doi">10.1093/bioinformatics/bts364</idno><idno type="wicri:Area/Main/Corpus">000417</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">000417</idno><idno type="wicri:Area/Main/Curation">000417</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Curation">000417</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">A bioimage informatics approach to automatically extract complex fungal networks.</title><author><name sortKey="Obara, Boguslaw" sort="Obara, Boguslaw" uniqKey="Obara B" first="Boguslaw" last="Obara">Boguslaw Obara</name><affiliation wicri:level="1"><nlm:affiliation>Oxford e-Research Centre, University of Oxford, UK. boguslaw.obara@oerc.ox.ac.uk</nlm:affiliation><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>Oxford e-Research Centre, University of Oxford</wicri:regionArea></affiliation></author><author><name sortKey="Grau, Vicente" sort="Grau, Vicente" uniqKey="Grau V" first="Vicente" last="Grau">Vicente Grau</name></author><author><name sortKey="Fricker, Mark D" sort="Fricker, Mark D" uniqKey="Fricker M" first="Mark D" last="Fricker">Mark D. Fricker</name></author></analytic><series><title level="j">Bioinformatics (Oxford, England)</title><idno type="eISSN">1367-4811</idno><imprint><date when="2012" type="published">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms (MeSH)</term><term>Fungi (cytology)</term><term>Image Processing, Computer-Assisted (methods)</term><term>Mycelium (cytology)</term><term>Phanerochaete (cytology)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes (MeSH)</term><term>Champignons (cytologie)</term><term>Mycelium (cytologie)</term><term>Phanerochaete (cytologie)</term><term>Traitement d'image par ordinateur (méthodes)</term></keywords><keywords scheme="MESH" qualifier="cytologie" xml:lang="fr"><term>Champignons</term><term>Mycelium</term><term>Phanerochaete</term></keywords><keywords scheme="MESH" qualifier="cytology" xml:lang="en"><term>Fungi</term><term>Mycelium</term><term>Phanerochaete</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Image Processing, Computer-Assisted</term></keywords><keywords scheme="MESH" qualifier="méthodes" xml:lang="fr"><term>Traitement d'image par ordinateur</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>MOTIVATION</b></p><p>Fungi form extensive interconnected mycelial networks that scavenge efficiently for scarce resources in a heterogeneous environment. The architecture of the network is highly responsive to local nutritional cues, damage or predation, and continuously adapts through growth, branching, fusion or regression. These networks also provide an example of an experimental planar network system that can be subjected to both theoretical analysis and experimental manipulation in multiple replicates. For high-throughput measurements, with hundreds of thousands of branches on each image, manual detection is not a realistic option, especially if extended time series are captured. Furthermore, branches typically show considerable variation in contrast as the individual cords span several orders of magnitude and the compressed soil substrate is not homogeneous in texture making automated segmentation challenging.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>We have developed and evaluated a high-throughput automated image analysis and processing approach using Phase Congruency Tensors and watershed segmentation to characterize complex fungal networks. The performance of the proposed approach is evaluated using complex images of saprotrophic fungal networks with 10(5)-10(6) edges. The results obtained demonstrate that this approach provides a fast and robust solution for detection and graph-based representation of complex curvilinear networks.</p></div><div type="abstract" xml:lang="en"><p><b>AVAILABILITY AND IMPLEMENTATION</b></p><p>The Matlab toolbox is freely available through the Oxford e-Research Centre website: http://www.oerc.ox.ac.uk/research/bioimage/software</p></div><div type="abstract" xml:lang="en"><p><b>CONTACTS</b></p><p>boguslaw.obara@oerc.ox.ac.uk.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">22743223</PMID><DateCompleted><Year>2013</Year><Month>05</Month><Day>15</Day></DateCompleted><DateRevised><Year>2012</Year><Month>09</Month><Day>10</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1367-4811</ISSN><JournalIssue CitedMedium="Internet"><Volume>28</Volume><Issue>18</Issue><PubDate><Year>2012</Year><Month>Sep</Month><Day>15</Day></PubDate></JournalIssue><Title>Bioinformatics (Oxford, England)</Title><ISOAbbreviation>Bioinformatics</ISOAbbreviation></Journal><ArticleTitle>A bioimage informatics approach to automatically extract complex fungal networks.</ArticleTitle><Pagination><MedlinePgn>2374-81</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1093/bioinformatics/bts364</ELocationID><Abstract><AbstractText Label="MOTIVATION" NlmCategory="BACKGROUND">Fungi form extensive interconnected mycelial networks that scavenge efficiently for scarce resources in a heterogeneous environment. The architecture of the network is highly responsive to local nutritional cues, damage or predation, and continuously adapts through growth, branching, fusion or regression. These networks also provide an example of an experimental planar network system that can be subjected to both theoretical analysis and experimental manipulation in multiple replicates. For high-throughput measurements, with hundreds of thousands of branches on each image, manual detection is not a realistic option, especially if extended time series are captured. Furthermore, branches typically show considerable variation in contrast as the individual cords span several orders of magnitude and the compressed soil substrate is not homogeneous in texture making automated segmentation challenging.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">We have developed and evaluated a high-throughput automated image analysis and processing approach using Phase Congruency Tensors and watershed segmentation to characterize complex fungal networks. The performance of the proposed approach is evaluated using complex images of saprotrophic fungal networks with 10(5)-10(6) edges. The results obtained demonstrate that this approach provides a fast and robust solution for detection and graph-based representation of complex curvilinear networks.</AbstractText><AbstractText Label="AVAILABILITY AND IMPLEMENTATION" NlmCategory="METHODS">The Matlab toolbox is freely available through the Oxford e-Research Centre website: http://www.oerc.ox.ac.uk/research/bioimage/software</AbstractText><AbstractText Label="CONTACTS" NlmCategory="BACKGROUND">boguslaw.obara@oerc.ox.ac.uk.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Obara</LastName><ForeName>Boguslaw</ForeName><Initials>B</Initials><AffiliationInfo><Affiliation>Oxford e-Research Centre, University of Oxford, UK. boguslaw.obara@oerc.ox.ac.uk</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Grau</LastName><ForeName>Vicente</ForeName><Initials>V</Initials></Author><Author ValidYN="Y"><LastName>Fricker</LastName><ForeName>Mark D</ForeName><Initials>MD</Initials></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><Agency>Biotechnology and Biological Sciences Research Council</Agency><Country>United Kingdom</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2012</Year><Month>06</Month><Day>27</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Bioinformatics</MedlineTA><NlmUniqueID>9808944</NlmUniqueID><ISSNLinking>1367-4803</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000465" MajorTopicYN="N">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005658" MajorTopicYN="N">Fungi</DescriptorName><QualifierName UI="Q000166" MajorTopicYN="Y">cytology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D007091" MajorTopicYN="N">Image Processing, Computer-Assisted</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D025282" MajorTopicYN="N">Mycelium</DescriptorName><QualifierName UI="Q000166" MajorTopicYN="Y">cytology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D020075" MajorTopicYN="N">Phanerochaete</DescriptorName><QualifierName UI="Q000166" MajorTopicYN="N">cytology</QualifierName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2012</Year><Month>6</Month><Day>30</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2012</Year><Month>6</Month><Day>30</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2013</Year><Month>5</Month><Day>17</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">22743223</ArticleId><ArticleId IdType="pii">bts364</ArticleId><ArticleId IdType="doi">10.1093/bioinformatics/bts364</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Bois/explor/PhanerochaeteV1/Data/Main/Curation
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000417 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Main/Curation/biblio.hfd -nk 000417 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Bois
|area= PhanerochaeteV1
|flux= Main
|étape= Curation
|type= RBID
|clé= pubmed:22743223
|texte= A bioimage informatics approach to automatically extract complex fungal networks.
}}
Pour générer des pages wiki
HfdIndexSelect -h $EXPLOR_AREA/Data/Main/Curation/RBID.i -Sk "pubmed:22743223" \
| HfdSelect -Kh $EXPLOR_AREA/Data/Main/Curation/biblio.hfd \
| NlmPubMed2Wicri -a PhanerochaeteV1
| This area was generated with Dilib version V0.6.37. |  |