A Robust Adaptive Wavelet-based Method for Classification of Meningioma Histology Images
Qureshi H, Rajpoot N, Nattkemper TW, Hans V (2009)
In: MICCAI«2009 Workshop on Optical Tissue Image Analysis in Microscopy, Histology, and Endoscopy.
Konferenzbeitrag
| Veröffentlicht | Englisch
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Autor*in
Qureshi, Hammad;
Rajpoot, Nasir;
Nattkemper, Tim WilhelmUniBi 
;
Hans, Volkmar
;
Hans, VolkmarEinrichtung
Abstract / Bemerkung
Intra-class variability in the texture of samples is an im- portant problem in the domain of histological image classi?cation. This issue is inherent to the ?eld due to the high complexity of histology im- age data. A technique that provides good results in one trial may fail in another when the test and training data are changed and therefore, the technique needs to be adapted for intra-class texture variation. In this paper, we present a novel wavelet based multiresolution analysis approach to meningioma subtype classi?cation in response to the chal- lenge of data variation. We analyze the stability of Adaptive Discriminant Wavelet Packet Transform (ADWPT) and present a solution to the issue of variation in the ADWPT decomposition when texture in data changes. A feature selection approach is proposed that provides high classi?cation accuracy.
Stichworte
Tissue;
Feature extraction;
classification;
histopathology;
Medical Imaging;
pathology;
wavelets
Erscheinungsjahr
2009
Titel des Konferenzbandes
MICCAI«2009 Workshop on Optical Tissue Image Analysis in Microscopy, Histology, and Endoscopy
Konferenz
OPTIMHisE
Konferenzort
London, UK
Page URI
https://pub.uni-bielefeld.de/record/2018427
Zitieren
Qureshi H, Rajpoot N, Nattkemper TW, Hans V. A Robust Adaptive Wavelet-based Method for Classification of Meningioma Histology Images. In: MICCAI«2009 Workshop on Optical Tissue Image Analysis in Microscopy, Histology, and Endoscopy. 2009.
Qureshi, H., Rajpoot, N., Nattkemper, T. W., & Hans, V. (2009). A Robust Adaptive Wavelet-based Method for Classification of Meningioma Histology Images. MICCAI«2009 Workshop on Optical Tissue Image Analysis in Microscopy, Histology, and Endoscopy
Qureshi, Hammad, Rajpoot, Nasir, Nattkemper, Tim Wilhelm, and Hans, Volkmar. 2009. “A Robust Adaptive Wavelet-based Method for Classification of Meningioma Histology Images”. In MICCAI«2009 Workshop on Optical Tissue Image Analysis in Microscopy, Histology, and Endoscopy.
Qureshi, H., Rajpoot, N., Nattkemper, T. W., and Hans, V. (2009). “A Robust Adaptive Wavelet-based Method for Classification of Meningioma Histology Images” in MICCAI«2009 Workshop on Optical Tissue Image Analysis in Microscopy, Histology, and Endoscopy.
Qureshi, H., et al., 2009. A Robust Adaptive Wavelet-based Method for Classification of Meningioma Histology Images. In MICCAI«2009 Workshop on Optical Tissue Image Analysis in Microscopy, Histology, and Endoscopy.
H. Qureshi, et al., “A Robust Adaptive Wavelet-based Method for Classification of Meningioma Histology Images”, MICCAI«2009 Workshop on Optical Tissue Image Analysis in Microscopy, Histology, and Endoscopy, 2009.
Qureshi, H., Rajpoot, N., Nattkemper, T.W., Hans, V.: A Robust Adaptive Wavelet-based Method for Classification of Meningioma Histology Images. MICCAI«2009 Workshop on Optical Tissue Image Analysis in Microscopy, Histology, and Endoscopy. (2009).
Qureshi, Hammad, Rajpoot, Nasir, Nattkemper, Tim Wilhelm, and Hans, Volkmar. “A Robust Adaptive Wavelet-based Method for Classification of Meningioma Histology Images”. MICCAI«2009 Workshop on Optical Tissue Image Analysis in Microscopy, Histology, and Endoscopy. 2009.
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