A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to statistical pattern recognition (2nd ed.)
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Contrast limited adaptive histogram equalization
Graphics gems IV
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color Texture Classification by Wavelet Energy Correlation Signatures
ICIAP '97 Proceedings of the 9th International Conference on Image Analysis and Processing-Volume I - Volume I
Textons, Contours and Regions: Cue Integration in Image Segmentation
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Robust Texture Classification by Subsets of Local Binary Patterns
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Shape Localization Based on Statistical Method Using Extended Local Binary Pattern
ICIG '04 Proceedings of the Third International Conference on Image and Graphics
Color and Position versus Texture Features for Endoscopic Polyp Detection
BMEI '08 Proceedings of the 2008 International Conference on BioMedical Engineering and Informatics - Volume 02
Multiresolution image parametrization for improving texture classification
EURASIP Journal on Advances in Signal Processing
Automated classification of duodenal imagery in celiac disease using evolved Fourier feature vectors
Computer Methods and Programs in Biomedicine
A Blind Image Watermarking Scheme Based on Wavelet Tree Quantization
ISECS '09 Proceedings of the 2009 Second International Symposium on Electronic Commerce and Security - Volume 01
Pattern Analysis & Applications
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Texture representation in AAM using Gabor wavelet and local binary patterns
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Enhanced local texture feature sets for face recognition under difficult lighting conditions
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
Computer Methods and Programs in Biomedicine
WAMUS'07 Proceedings of the 7th WSEAS international conference on Wavelet analysis & multirate systems
Image processing and machine learning for fully automated probabilistic evaluation of medical images
Computer Methods and Programs in Biomedicine
Computer-aided tumor detection in endoscopic video using color wavelet features
IEEE Transactions on Information Technology in Biomedicine
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
MCBR-CDS'12 Proceedings of the Third MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
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Automated classification of duodenal texture patches with histological ground truth in case of pediatric celiac disease is proposed. The classical focus of classification in this context is a two-class problem: mucosa affected by celiac disease and unaffected duodenal tissue. We extend this focus and apply classification according to a modified Marsh scheme into four classes. In addition to other techniques used previously for classification of endoscopic imagery, we apply local binary pattern (LBP) operators and propose two new operator types, one of which adapts to the different properties of wavelet transform subbands. The achieved results are promising in that operators based on LBP turn out to achieve better results compared to many other texture classification techniques as used in earlier work. Specifically, the proposed wavelet-based LBP scheme achieved the best overall accuracy of all feature extraction techniques considered in the two-class case and was among the best in the four-class scheme. Results also show that a classification into four classes is feasible in principle however when compared to the two-class case we note that there is still room for improvement due to various reasons discussed.