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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Histopathological image analysis for centroblasts classification
through dimensionality reduction approaches</title><author><name sortKey="Kornaropoulos, Evgenios N" sort="Kornaropoulos, Evgenios N" uniqKey="Kornaropoulos E" first="Evgenios N." last="Kornaropoulos">Evgenios N. Kornaropoulos</name><affiliation><nlm:aff id="A1">Informatics and Telematics Institute - Centre for Research and Technology Hellas (ITI - CERTH), Thessaloniki, Greece</nlm:aff></affiliation></author><author><name sortKey="Niazi, M Khalid Khan" sort="Niazi, M Khalid Khan" uniqKey="Niazi M" first="M Khalid Khan" last="Niazi">M Khalid Khan Niazi</name><affiliation><nlm:aff id="A2">Ohio State University, Department of Biomedical Informatics, Columbus, Ohio, United States of America (USA)</nlm:aff></affiliation></author><author><name sortKey="Lozanski, Gerard" sort="Lozanski, Gerard" uniqKey="Lozanski G" first="Gerard" last="Lozanski">Gerard Lozanski</name><affiliation><nlm:aff id="A3">Ohio State University, Department of Pathology, Columbus, Ohio, United States of America (USA)</nlm:aff></affiliation></author><author><name sortKey="Gurcan, Metin N" sort="Gurcan, Metin N" uniqKey="Gurcan M" first="Metin N." last="Gurcan">Metin N. Gurcan</name><affiliation><nlm:aff id="A2">Ohio State University, Department of Biomedical Informatics, Columbus, Ohio, United States of America (USA)</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">24376080</idno><idno type="pmc">4017952</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4017952</idno><idno type="RBID">PMC:4017952</idno><idno type="doi">10.1002/cyto.a.22432</idno><date when="2013">2013</date><idno type="wicri:Area/Pmc/Corpus">000412</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000412</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Histopathological image analysis for centroblasts classification
through dimensionality reduction approaches</title><author><name sortKey="Kornaropoulos, Evgenios N" sort="Kornaropoulos, Evgenios N" uniqKey="Kornaropoulos E" first="Evgenios N." last="Kornaropoulos">Evgenios N. Kornaropoulos</name><affiliation><nlm:aff id="A1">Informatics and Telematics Institute - Centre for Research and Technology Hellas (ITI - CERTH), Thessaloniki, Greece</nlm:aff></affiliation></author><author><name sortKey="Niazi, M Khalid Khan" sort="Niazi, M Khalid Khan" uniqKey="Niazi M" first="M Khalid Khan" last="Niazi">M Khalid Khan Niazi</name><affiliation><nlm:aff id="A2">Ohio State University, Department of Biomedical Informatics, Columbus, Ohio, United States of America (USA)</nlm:aff></affiliation></author><author><name sortKey="Lozanski, Gerard" sort="Lozanski, Gerard" uniqKey="Lozanski G" first="Gerard" last="Lozanski">Gerard Lozanski</name><affiliation><nlm:aff id="A3">Ohio State University, Department of Pathology, Columbus, Ohio, United States of America (USA)</nlm:aff></affiliation></author><author><name sortKey="Gurcan, Metin N" sort="Gurcan, Metin N" uniqKey="Gurcan M" first="Metin N." last="Gurcan">Metin N. Gurcan</name><affiliation><nlm:aff id="A2">Ohio State University, Department of Biomedical Informatics, Columbus, Ohio, United States of America (USA)</nlm:aff></affiliation></author></analytic><series><title level="j">Cytometry. Part A : the journal of the International Society for Analytical Cytology</title><idno type="ISSN">1552-4922</idno><idno type="eISSN">1552-4930</idno><imprint><date when="2013">2013</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p id="P1">We present two novel automated image analysis methods to differentiate
centroblast (CB) cells from non-centroblast (Non-CB) cells in digital images of
H&E-stained tissues of follicular lymphoma. CB cells are often confused
by similar looking cells within the tissue, therefore a system to help their
classification is necessary. Our methods extract the discriminatory features of
cells by approximating the intrinsic dimensionality from the subspace spanned by
CB and Non-CB cells. In the first method, discriminatory features are
approximated with the help of Singular Value Decomposition (SVD), whereas in the
second method they are extracted using Laplacian Eigenmaps. Five hundred
high-power field images were extracted from 17 slides, which are then used to
compose a database of 213 CB and 234 Non-CB region of interest images. The
recall, precision and overall accuracy rates of the developed methods were
measured and compared with existing classification methods. Moreover, the
reproducibility of both classification methods was also examined. The average
values of the overall accuracy were 99.22% ± 0.75% and
99.07% ± 1.53% for COB and CLEM, respectively. The
experimental results demonstrate that both proposed methods provide better
classification accuracy of CB/Non-CB in comparison to the state of the art
methods.</p></div></front></TEI><pmc article-type="research-article"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
<pmc-dir>properties manuscript</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-journal-id">101235694</journal-id><journal-id journal-id-type="pubmed-jr-id">32205</journal-id><journal-id journal-id-type="nlm-ta">Cytometry A</journal-id><journal-id journal-id-type="iso-abbrev">Cytometry A</journal-id><journal-title-group><journal-title>Cytometry. Part A : the journal of the International Society for Analytical Cytology</journal-title></journal-title-group><issn pub-type="ppub">1552-4922</issn><issn pub-type="epub">1552-4930</issn></journal-meta><article-meta><article-id pub-id-type="pmid">24376080</article-id><article-id pub-id-type="pmc">4017952</article-id><article-id pub-id-type="doi">10.1002/cyto.a.22432</article-id><article-id pub-id-type="manuscript">NIHMS553326</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Histopathological image analysis for centroblasts classification
through dimensionality reduction approaches</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Kornaropoulos</surname><given-names>Evgenios N.</given-names></name><xref ref-type="aff" rid="A1">1</xref><xref rid="FN1" ref-type="author-notes">*</xref></contrib><contrib contrib-type="author"><name><surname>Niazi</surname><given-names>M Khalid Khan</given-names></name><xref ref-type="aff" rid="A2">2</xref></contrib><contrib contrib-type="author"><name><surname>Lozanski</surname><given-names>Gerard</given-names></name><xref ref-type="aff" rid="A3">3</xref><xref rid="FN2" ref-type="author-notes">†</xref></contrib><contrib contrib-type="author"><name><surname>Gurcan</surname><given-names>Metin N.</given-names></name><xref ref-type="aff" rid="A2">2</xref><xref rid="FN2" ref-type="author-notes">†</xref><xref rid="FN3" ref-type="author-notes">‡</xref></contrib></contrib-group><aff id="A1"><label>1</label>Informatics and Telematics Institute - Centre for Research and Technology Hellas (ITI - CERTH), Thessaloniki, Greece</aff><aff id="A2"><label>2</label>Ohio State University, Department of Biomedical Informatics, Columbus, Ohio, United States of America (USA)</aff><aff id="A3"><label>3</label>Ohio State University, Department of Pathology, Columbus, Ohio, United States of America (USA)</aff><author-notes><corresp id="FN1"><label>*</label>Corresponding author: E. N. Kornaropoulos
(<email>ekornaro@gmail.com</email>), Department of Computer Vision,
Informatics and Telematics Institute - Centre for Research and Technology Hellas
(ITI - CERTH), 1st km Thermi - Panorama, 57001, Thessaloniki, Greece</corresp><fn id="FN2" fn-type="equal"><label>†</label><p>Gerard Lozanski and Metin N. Gurcan are both senior authors and contributed
equally to this paper.</p></fn><fn id="FN3" fn-type="other"><label>‡</label><p>Senior Member, IEEE</p></fn></author-notes><pub-date pub-type="nihms-submitted"><day>11</day><month>3</month><year>2014</year></pub-date><pub-date pub-type="epub"><day>26</day><month>12</month><year>2013</year></pub-date><pub-date pub-type="ppub"><month>3</month><year>2014</year></pub-date><pub-date pub-type="pmc-release"><day>01</day><month>3</month><year>2015</year></pub-date><volume>85</volume><issue>3</issue><fpage>242</fpage><lpage>255</lpage><pmc-comment>elocation-id from pubmed: 10.1002/cyto.a.22432</pmc-comment>
<abstract><p id="P1">We present two novel automated image analysis methods to differentiate
centroblast (CB) cells from non-centroblast (Non-CB) cells in digital images of
H&E-stained tissues of follicular lymphoma. CB cells are often confused
by similar looking cells within the tissue, therefore a system to help their
classification is necessary. Our methods extract the discriminatory features of
cells by approximating the intrinsic dimensionality from the subspace spanned by
CB and Non-CB cells. In the first method, discriminatory features are
approximated with the help of Singular Value Decomposition (SVD), whereas in the
second method they are extracted using Laplacian Eigenmaps. Five hundred
high-power field images were extracted from 17 slides, which are then used to
compose a database of 213 CB and 234 Non-CB region of interest images. The
recall, precision and overall accuracy rates of the developed methods were
measured and compared with existing classification methods. Moreover, the
reproducibility of both classification methods was also examined. The average
values of the overall accuracy were 99.22% ± 0.75% and
99.07% ± 1.53% for COB and CLEM, respectively. The
experimental results demonstrate that both proposed methods provide better
classification accuracy of CB/Non-CB in comparison to the state of the art
methods.</p></abstract><kwd-group><kwd>Follicular lymphoma</kwd><kwd>dimensionality reduction</kwd><kwd>intrinsic dimensionality</kwd><kwd>SVD</kwd><kwd>LDA</kwd><kwd>Laplacian Eigenmaps</kwd></kwd-group></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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