Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Localization Based on Statistical Method Using Extended Local Binary Pattern
ICIG '04 Proceedings of the Third International Conference on Image and Graphics
Texture analysis for ulcer detection in capsule endoscopy images
Image and Vision Computing
Texture Image Retrieval Using Non-separable Wavelets and Local Binary Patterns
CIS '09 Proceedings of the 2009 International Conference on Computational Intelligence and Security - Volume 01
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
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
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
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Automated Marsh-like classification of celiac disease in children using local texture operators
Computers in Biology and Medicine
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
MCBR-CDS'12 Proceedings of the Third MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
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Local Binary Patterns (LBP) is a widely used approach for medical image analysis. Limitations of the LBP operator are its sensitivity to noise and its boundedness to first derivative information. These limitations are usually balanced by extensions of the classical LBP operator (e.g. the Local Ternary Pattern operator (LTP) or the Extended LBP (ELBP) operator). In this paper we present a generic framework that is able to overcome this limitations by frequency filtering the images as pre-processing stage to the classical LBP. The advantage of this approach is its easier adaption and optimization to different application scenarios and data sets as compared to other LBP variants. Experiments are carried out employing two endoscopic data sets, the first from the duodenum used for diagnosis of celiac disease, the second from the colon used for polyp malignity assessment. It turned out that high pass filtering combined with LBP outperforms classical LBP and most of its extensions, whereas low pass filtering effects the results only to a small extent.