Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
In-Vivo IVUS tissue classification: a comparison between RF signal analysis and reconstructed images
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Automatic IVUS segmentation of atherosclerotic plaque with stop & go snake
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Efficient and effective ultrasound image analysis scheme for thyroid nodule detection
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Combining different local binary pattern variants to boost performance
Expert Systems with Applications: An International Journal
Random interest regions for object recognition based on texture descriptors and bag of features
Expert Systems with Applications: An International Journal
Survey on LBP based texture descriptors for image classification
Expert Systems with Applications: An International Journal
TND: A Thyroid Nodule Detection System for Analysis of Ultrasound Images and Videos
Journal of Medical Systems
Local fuzzy pattern: a new way for micro-pattern analysis
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
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B-scan ultrasound provides a non-invasive low-cost imaging solution to primary care diagnostics. The inherent speckle noise in the images produced by this technique introduces uncertainty in the representation of their textural characteristics. To cope with the uncertainty, we propose a novel fuzzy feature extraction method to encode local texture. The proposed method extends the Local Binary Pattern (LBP) approach by incorporating fuzzy logic in the representation of local patterns of texture in ultrasound images. Fuzzification allows a Fuzzy Local Binary Pattern (FLBP) to contribute to more than a single bin in the distribution of the LBP values used as a feature vector. The proposed FLBP approach was experimentally evaluated for supervised classification of nodular and normal samples from thyroid ultrasound images. The results validate its effectiveness over LBP and other common feature extraction methods.