Fusion of fuzzy statistical distributions for classification of thyroid ultrasound patterns.
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.


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.
Iakovidis
Dimitris K
DK
Department of Informatics and Computer Technology, Technological Educational Institute of Lamia, Lamia, Greece. dimitris.iakovidis@ieee.org
Keramidas
Eystratios G
EG
Maroulis
Dimitris
D
D000465:Algorithms; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D016002:Discriminant Analysis; D017143:Fuzzy Logic; D006115:Greece; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008490:Medical Informatics; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D011237:Predictive Value of Tests; D011379:Prognosis; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D013964:Thyroid Neoplasms / Q000000981:diagnostic imaging*; D016606:Thyroid Nodule / Q000000981:diagnostic imaging*; D014463:Ultrasonography