Paper Info
Title
Adaptive discriminant wavelet packet transform and local binary patterns for meningioma subtype classification.
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.
Year
DOI
Venue
2008
10.1007/978-3-540-85990-1_24
MICCAI (2)
Keywords
Field
DocType
subband texture,meningioma subtype classification,combined approach,meningioma subtype,adaptive discriminant wavelet packet,higher overall classification accuracy,lbp feature,high classification accuracy,local binary patterns,higher accuracy,adaptive wavelet packet transform,classification performance,local binary pattern,it management,wavelet packet transform
Computer vision,Dimensionality reduction,Pattern recognition,Discriminant,Computer science,Local binary patterns,Support vector machine,Artificial intelligence,Macro,Wavelet packet decomposition
Conference
Volume
Issue
ISSN
11
Pt 2
0302-9743
Citations 
PageRank 
References 
17
1.13
10
Authors
5
Name
Order
Citations
PageRank
Hammad Qureshi1253.07
Olcay Sertel235124.21
Nasir Rajpoot354446.45
Roland Wilson4171.13
Metin Gurcan5564.28