A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
Filters
Adaptive Discriminant Wavelet Packet Transform and Local Binary Patterns for Meningioma Subtype Classification
[chapter]
2008
Lecture Notes in Computer Science
Preserved Fulltext
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
2009
2009 16th IEEE International Conference on Image Processing (ICIP)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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
2015
Computerized Medical Imaging and Graphics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2015; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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
2017
International Journal of Advanced Computer Science and Applications
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
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
2010
Pattern Recognition
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2014; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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
2009
IEEE Reviews in Biomedical Engineering
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2014; you can also visit the original URL.
The file type is application/pdf.
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