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

Authors:
Hammad Qureshi;Olcay Sertel;Nasir Rajpoot;Roland Wilson;Metin Gurcan
Affiliations:
Department of Computer Science, University of Warwick, United Kingdom;Department of Biomedical Informatics, The Ohio State University, United States;Department of Computer Science, University of Warwick, United Kingdom;Department of Computer Science, University of Warwick, United Kingdom;Department of Biomedical Informatics, The Ohio State University, United States
Venue:
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Year:
2008

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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.