HEp-2 Cell Classification Using Multi-resolution Local Patterns and Ensemble SVMs

Siyamalan Manivannan (Lead / Corresponding author), Wenqi Li, Shazia Akbar, Ruixuan Wang, Jianguo Zhang, Stephen J. McKenna

Research output: Chapter in Book/Report/Conference proceedingConference contribution

29 Citations (Scopus)

Abstract

We describe a pattern recognition system for classifying immunofluorescence images of HEp-2 cells into six classes: homogeneous, speckled, nucleolar, centromere, golgi, and nuclear membrane. We use locality-constrained linear coding to encode multiple local features and two-level cell pyramids to capture spatial structure of cells. An ensemble of linear support vector machines is used to classify each cell image. Leave-one-specimen-out experiments on the I3A Contest Task 1 training data set predicted a mean class accuracy of 80.25%.

Original languageEnglish
Title of host publicationProceedings - 2014 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images. I3A Workshop 2014
PublisherIEEE Computer Society
Pages37-40
Number of pages4
ISBN (Print)9781479942527
DOIs
Publication statusPublished - 2014
Event13A: 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images - Stockholm Waterfront, Stockholm, Sweden
Duration: 24 Aug 201424 Aug 2014
http://i3a2014.unisa.it/

Workshop

Workshop13A: 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images
CountrySweden
CityStockholm
Period24/08/1424/08/14
Internet address

Keywords

  • HEp-2 Cell Classification
  • multi-resolution local patterns
  • pattern recognition

Fingerprint Dive into the research topics of 'HEp-2 Cell Classification Using Multi-resolution Local Patterns and Ensemble SVMs'. Together they form a unique fingerprint.

  • Student Theses

    Analysis of Colorectal Polyps in Optical Projection Tomography

    Author: Li, W., 2015

    Supervisor: Zhang, J. (Supervisor) & McKenna, S. (Supervisor)

    Student thesis: Doctoral ThesisDoctor of Philosophy

    File

    Cite this

    Manivannan, S., Li, W., Akbar, S., Wang, R., Zhang, J., & McKenna, S. J. (2014). HEp-2 Cell Classification Using Multi-resolution Local Patterns and Ensemble SVMs. In Proceedings - 2014 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images. I3A Workshop 2014 (pp. 37-40). IEEE Computer Society. https://doi.org/10.1109/I3A.2014.18