Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Help & Advice
University of Warwick

The Library

  • Login
  • Admin

A global covariance descriptor for nuclear atypia scoring in breast histopathology images

Tools
- Tools
+ Tools

Khan, Adnan M., Sirinukunwattana, Korsuk and Rajpoot, Nasir M. (2015) A global covariance descriptor for nuclear atypia scoring in breast histopathology images. IEEE Journal of Biomedical and Health Informatics, 19 (5). 1637 -1647. doi:10.1109/JBHI.2015.2447008 ISSN 2168-2194.

[img] PDF
WRAP_0070699-cs-070715-khansirinukunwattanarajpoot_jbhi2015.pdf - Accepted Version
Embargoed item. Restricted access to Repository staff only - Requires a PDF viewer.

Download (9Mb)
Official URL: http://dx.doi.org/10.1109/JBHI.2015.2447008

Request Changes to record.

Abstract

Nuclear 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.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
R Medicine > R Medicine (General)
R Medicine > RC Internal medicine
R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Science, Engineering and Medicine > Science > Computer Science
Library of Congress Subject Headings (LCSH): Breast--Cancer, Cancer--Histopathology, Diagnostic imaging--Digital techniques, Imaging systems in medicine, Optical pattern recognition
Journal or Publication Title: IEEE Journal of Biomedical and Health Informatics
Publisher: IEEE
ISSN: 2168-2194
Official Date: September 2015
Dates:
DateEvent
September 2015Published
18 June 2015Available
13 June 2015Accepted
Volume: 19
Number: 5
Page Range: 1637 -1647
DOI: 10.1109/JBHI.2015.2447008
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Date of first compliant deposit: 30 December 2015
Funder: Qatar National Research Fund (QNRF), University of Warwick Postgraduate Research Scholarship, Department of Computer Science, University of Warwick
Grant number: NPRP 5-1345-1-228 (QNRF)

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item
twitter

Email us: wrap@warwick.ac.uk
Contact Details
About Us