Essentials of fuzzy modeling and control
Essentials of fuzzy modeling and control
Incorporation of gray-level imprecision in representation and processing of digital images
Pattern Recognition Letters
Segmentation of Meningiomas and Low Grade Gliomas in MRI
MICCAI '99 Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention
Different types of image texture features in ultrasound of patients with lymphocytic thyroiditis
ISICT '03 Proceedings of the 1st international symposium on Information and communication technologies
Using AUC and Accuracy in Evaluating Learning Algorithms
IEEE Transactions on Knowledge and Data Engineering
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Computational Characterization of Thyroid Tissue in the Radon Domain
CBMS '07 Proceedings of the Twentieth IEEE International Symposium on Computer-Based Medical Systems
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Pathological Image Analysis Using the GPU: Stroma Classification for Neuroblastoma
BIBM '07 Proceedings of the 2007 IEEE International Conference on Bioinformatics and Biomedicine
Thyroid Texture Representation via Noise Resistant Image Features
CBMS '08 Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems
Random Texture Defect Detection Using 1-D Hidden Markov Models Based on Local Binary Patterns
IEICE - Transactions on Information and Systems
AUC: a better measure than accuracy in comparing learning algorithms
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Guest editorial: Knowledge discovery and computer-based decision support in biomedicine
Artificial Intelligence in Medicine
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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Objective: This paper proposes a novel approach for thyroid ultrasound pattern representation. Considering that texture and echogenicity are correlated with thyroid malignancy, the proposed approach encodes these sonographic features via a noise-resistant representation. This representation is suitable for the discrimination of nodules of high malignancy risk from normal thyroid parenchyma. Materials and methods: The material used in this study includes a total of 250 thyroid ultrasound patterns obtained from 75 patients in Greece. The patterns are represented by fused vectors of fuzzy features. Ultrasound texture is represented by fuzzy local binary patterns, whereas echogenicity is represented by fuzzy intensity histograms. The encoded thyroid ultrasound patterns are discriminated by support vector classifiers. Results: The proposed approach was comprehensively evaluated using receiver operating characteristics (ROCs). The results show that the proposed fusion scheme outperforms previous thyroid ultrasound pattern representation methods proposed in the literature. The best classification accuracy was obtained with a polynomial kernel support vector machine, and reached 97.5% as estimated by the area under the ROC curve. Conclusions: The fusion of fuzzy local binary patterns and fuzzy grey-level histogram features is more effective than the state of the art approaches for the representation of thyroid ultrasound patterns and can be effectively utilized for the detection of nodules of high malignancy risk in the context of an intelligent medical system.