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Adaptive Discriminant Wavelet Packet Transform and Local Binary Patterns for Meningioma Subtype Classification

  • Hammad Qureshi
  • Olcay Sertel
  • Nasir Rajpoot
  • Roland Wilson
  • Metin Gurcan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5242)

Abstract

The inherent complexity and non-homogeneity of texture makes classification in medical image analysis a challenging task. In this paper, we propose a combined approach for meningioma subtype classification using subband texture (macro) features and micro-texture features. These are captured using the Adaptive Wavelet Packet Transform (ADWPT) and Local Binary Patterns (LBPs), respectively. These two different textural features are combined together and used for classification. The effect of various dimensionality reduction techniques on classification performance is also investigated. We show that high classification accuracies can be achieved using ADWPT. Although LBP features do not provide higher overall classification accuracies than ADWPT, it manages to provide higher accuracy for a meningioma subtype that is difficult to classify otherwise.

Keywords

Support Vector Machine Local Binary Pattern Wavelet Packet Dimensionality Reduction Technique Wavelet Packet Transform 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Supplementary material

978-3-540-85990-1_24_MOESM1_ESM.zip (2.2 mb)
Electronic Supplementary Material (2,294 KB)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Hammad Qureshi
    • 1
  • Olcay Sertel
    • 2
  • Nasir Rajpoot
    • 1
  • Roland Wilson
    • 1
  • Metin Gurcan
    • 2
  1. 1.Department of Computer ScienceUniversity of WarwickUnited Kingdom
  2. 2.Department of Biomedical InformaticsThe Ohio State UniversityUnited States

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