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dc.contributor.authorKhan, AM
dc.contributor.authorSirinukunwattana, K
dc.contributor.authorRajpoot, N
dc.date.accessioned2018-07-19T13:38:32Z
dc.date.issued2015-09
dc.identifier5
dc.identifier.citationIEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2015, 19 pp. 1637 - 1647
dc.identifier.issn2168-2194
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/2097
dc.identifier.doi10.1109/JBHI.2015.2447008
dc.description.abstractNuclear atypia scoring is a diagnostic measure commonly used to assess tumor grade of various cancers, including breast cancer. It provides a quantitative measure of deviation in visual appearance of cell nuclei from those in normal epithelial cells. In this paper, we present a novel image-level descriptor for nuclear atypia scoring in breast cancer histopathology images. The method is based on the region covariance descriptor that has recently become a popular method in various computer vision applications. The descriptor in its original form is not suitable for classification of histopathology images as cancerous histopathology images tend to possess diversely heterogeneous regions in a single field of view. Our proposed image-level descriptor, which we term as the geodesic mean of region covariance descriptors, possesses all the attractive properties of covariance descriptors lending itself to tractable geodesic-distance-based k-nearest neighbor classification using efficient kernels. The experimental results suggest that the proposed image descriptor yields high classification accuracy compared to a variety of widely used image-level descriptors.
dc.format.extent1637 - 1647
dc.languageeng
dc.language.isoeng
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.titleA Global Covariance Descriptor for Nuclear Atypia Scoring in Breast Histopathology Images
dc.typeJournal Article
rioxxterms.versionofrecord10.1109/JBHI.2015.2447008
rioxxterms.licenseref.startdate2015-09
rioxxterms.typeJournal Article/Review
dc.relation.isPartOfIEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
pubs.notesaffiliation: Khan, AM (Reprint Author), Inst Canc Res, London SM2 5NG, England. Khan, Adnan Mujahid, Inst Canc Res, London SM2 5NG, England. Sirinukunwattana, Korsuk; Rajpoot, Nasir, Qatar Univ, Dept Comp Sci & Engn, Doha 2713, Qatar. Sirinukunwattana, Korsuk; Rajpoot, Nasir, Univ Warwick, Dept Comp Sci, Coventry CV4 7AL, W Midlands, England. keywords: Generalized geometric mean; histopathology images analysis; nuclear atypia (NA) scoring; region covariance (RC) descriptor; Riemannian manifold keywords-plus: LOCAL BINARY PATTERNS; TEXTURE CLASSIFICATION; PEDESTRIAN DETECTION; REGION COVARIANCE; FACE RECOGNITION; HISTOLOGY IMAGES; CANCER; FEATURES; SPACE; GRADE research-areas: Computer Science; Mathematical & Computational Biology; Medical Informatics web-of-science-categories: Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Medical Informatics author-email: [email protected] [email protected] [email protected] funding-acknowledgement: Qatar National Research Fund [NPRP 5-1345-1-228]; Warwick Postgraduate Research Scholarship Program; Department of Computer Science at the University of Warwick, U.K; Department of Computer Science, University of Warwick, U.K funding-text: This work was supported in part by the Qatar National Research Fund under Award NPRP 5-1345-1-228. The work of A. M. Khan was supported by the Warwick Postgraduate Research Scholarship Program and the Department of Computer Science at the University of Warwick, U.K. The work of K. Sirinukunwattana was supported in part by the Department of Computer Science, University of Warwick, U.K. Adnan Mujahid Khan and Korsuk Sirinukunwattana are co-first authors. number-of-cited-references: 54 times-cited: 5 usage-count-last-180-days: 1 usage-count-since-2013: 10 journal-iso: IEEE J. Biomed. Health Inform. doc-delivery-number: CQ7NP unique-id: ISI:000360791200013 da: 2018-07-19
pubs.notesNot known
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology/Computational Pathology & Integrated Genomics
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology/Computational Pathology & Integrated Genomics
pubs.volume19
pubs.embargo.termsNot known
icr.researchteamComputational Pathology & Integrated Genomicsen_US
dc.contributor.icrauthorKhan, Adnanen


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