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

Hammad Qureshi, Olcay Sertel, Nasir Rajpoot, Roland Wilson, Metin Gurcan
2008 Lecture Notes in Computer Science  
These are captured using the Adaptive Wavelet Packet Transform (ADWPT) and Local Binary Patterns (LBPs), respectively.  ...  In this paper, we propose a combined approach for meningioma subtype classification using subband texture (macro) features and micro-texture features.  ...  Acknowledgements The authors would like to acknowledge the support and guidance provided by Tim Nattkemper at the University of Bielefeld and Volkmar Hans at the Institute of Neuropathogy, Evangelisches  ... 
doi:10.1007/978-3-540-85990-1_24 fatcat:65ahjxx52ras5dpsyiyysjfieu

A fractal dimension based optimal wavelet packet analysis technique for classification of meningioma brain tumours

Omar S. Al-Kadi
2009 2009 16th IEEE International Conference on Image Processing (ICIP)  
An adaptive best bases algorithm for optimal bases selection for meningioma histopathological images is proposed, via applying the fractal dimension (FD) as the bases selection criterion in a tree-structured  ...  for classification.  ...  Also in another two studies, the performance of extracted features using adaptive WP transform was compared to local binary patterns [11] and to co-occurrence methods [12] via a support vector machine  ... 
doi:10.1109/icip.2009.5414534 dblp:conf/icip/Al-Kadi09 fatcat:ijvqihinuncadeekywrleyf2oe

A multiresolution clinical decision support system based on fractal model design for classification of histological brain tumours

Omar S. Al-Kadi
2015 Computerized Medical Imaging and Graphics  
A clinical decision support system that exploits the subband textural fractal characteristics for best bases selection of meningioma brain histopathological image classification is proposed.  ...  Our method outperformed the classical energy based selection approaches, achieving for SVM, Bayesian and kNN classifiers an overall classification accuracy of 94.12%, 92.50% and 79.70%, as compared to  ...  Volkmar Hans from the Institute of Neuropathology, Bielefeld, Germany for the provision of the meningioma dataset used in this paper.  ... 
doi:10.1016/j.compmedimag.2014.05.013 pmid:24962336 fatcat:ppkeqkrjh5gzxekj4ael2yf2l4

A Two Phase Hybrid Classifier based on Structure Similarities and Textural Features for Accurate Meningioma Classification

Kiran Fatima, Hammad Majeed
2017 International Journal of Advanced Computer Science and Applications  
Meningioma subtype classification is a complex pattern classification problem of digital pathology due to heterogeneity issues of tumor texture, low inter-class and high intra-class texture variations  ...  In this paper, a novel hybrid classification framework based on nuclei shape matching and texture analysis is proposed for classification of four subtypes of grade-I benign meningioma.  ...  Rajpoot, Associate Professor, Department of Computer Science, University of Warwick, United Kingdom for the provision of meningioma dataset of the Institute of Neuropathology, Bielefeld, Germany.  ... 
doi:10.14569/ijacsa.2017.080460 fatcat:uaz5tkk5wrfvhevpx4dpl6cbj4

Texture measures combination for improved meningioma classification of histopathological images

Omar S. Al-Kadi
2010 Pattern Recognition  
excluding highly correlated features, and a Bayesian classifier was used for meningioma subtype discrimination.  ...  The morphological gradient was applied to extract the region of interest for each subtype and for elimination of possible noise (e.g. cracks) which might occur during biopsy preparation.  ...  Volkmar Hans from the Institute of neuropathology in Bielefeld, Germany, for providing the histopathological data-set used in this work.  ... 
doi:10.1016/j.patcog.2010.01.005 fatcat:xbli2sk72fbdlftd2cfrwy4h44

Histopathological Image Analysis: A Review

M.N. Gurcan, L.E. Boucheron, A. Can, A. Madabhushi, N.M. Rajpoot, B. Yener
2009 IEEE Reviews in Biomedical Engineering  
This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe.  ...  In this paper, we review the recent state of the art CAD technology for digitized histopathology.  ...  four different subtypes of meningioma [101] . 2) Adaboost: The AdaBoost is an adaptive algorithm in the sense it combines a number of weak classifiers to generate a strong classifier.  ... 
doi:10.1109/rbme.2009.2034865 pmid:20671804 pmcid:PMC2910932 fatcat:a6sm4iy5gffbhlc23dtlp7xe2q