A Preliminary Analysis on HEp-2 Pattern Classification: Evaluating Strategies Based on Support Vector Machines and Subclass Discriminant Analysis

  • Ihtesham Ul Islam
  • Santa Di Cataldo
  • Andrea Bottino
  • Enrico Macii
  • Elisa Ficarra
Conference paper
First Online:
Part of the Communications in Computer and Information Science book series (CCIS, volume 452)

Abstract

The categorization of different staining patterns in HEp-2 cell slides by means of indirect immunofluorescence (IIF) is important for the differential diagnosis of autoimmune diseases. The clinical practice usually relies on the visual evaluation of the slides, which is time-consuming and subject to the specialist’s experience. Thus, there is a growing demand for computer-aided solutions capable of automatically classifying HEp-2 staining patterns. In the attempt to identify the most suited strategy for this task, in this work we compare two approaches based on Support Vector Machines and Subclass Discriminant Analysis. These techniques classify the available samples, characterized through a limited set of optimal textural attributes that are identified with a feature selection scheme. Our experimental results show that both strategies have a good concordance with the diagnosis of the human specialist and show the better performance of the Subclass Discriminant Analysis (91 % accuracy) compared to Support Vector Machines (87 % accuracy).

Keywords

Indirect immunofluorescence imaging HEp-2 staining pattern classification Support vector machines Subclass discriminant analysis Pattern recognition 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Ihtesham Ul Islam
    • 1
  • Santa Di Cataldo
    • 1
  • Andrea Bottino
    • 1
  • Enrico Macii
    • 1
  • Elisa Ficarra
    • 1
  1. 1.Department of Control and Computer EngineeringPolitecnico di TorinoTorinoItaly

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