An automated pattern recognition system for classifying indirect immunofluorescence images for HEp-2 cells and specimens

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

Research output: Contribution to journalArticle

58 Citations (Scopus)
354 Downloads (Pure)


Immunofluorescence antinuclear antibody tests are important for diagnosis and management of autoimmune conditions; a key step that would benefit from reliable automation is the recognition of subcellular patterns suggestive of different diseases. We present a system to recognize such patterns, at cellular and specimen levels, in images of HEp-2 cells. Ensembles of SVMs were trained to classify cells into six classes based on sparse encoding of texture features with cell pyramids, capturing spatial, multi-scale structure. A similar approach was used to classify specimens into seven classes. Software implementations were submitted to an international contest hosted by ICPR 2014 (Performance Evaluation of Indirect Immunofluorescence Image Analysis Systems). Mean class accuracies obtained on heldout test set were 87.1% and 88.5% for cell and specimenclassification respectively. These were the highest achieved in the competition, suggesting our methods are state-of-the-art. We provide detailed descriptions and extensive experiments with various features and encoding methods.
Original languageEnglish
Pages (from-to)12-26
Number of pages15
JournalPattern Recognition
Early online date28 Sep 2015
Publication statusPublished - Mar 2016


  • Anti-nuclear antibody test
  • cell classification
  • subcellular fluorescence patterns
  • HEp-2 cells
  • multi-resolution local patterns
  • Ensemble SVM

Fingerprint Dive into the research topics of 'An automated pattern recognition system for classifying indirect immunofluorescence images for HEp-2 cells and specimens'. 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



    No photo of Stephen McKenna

    McKenna, Stephen

    • Computing - Professor & Personal Chair of Computer Vision

    Person: Academic

    No photo of Jianguo Zhang

    Zhang, Jianguo

    Person: Associate Staff

    Cite this