Artif Intell Med - Fusion of fuzzy statistical distributions for classification of thyroid ultrasound patterns.

Tópicos

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Resumo

JECTIVE: 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.

Resumo Limpo

jectiv paper propos novel approach thyroid ultrasound pattern represent consid textur echogen correl thyroid malign propos approach encod sonograph featur via noiseresist represent represent suitabl discrimin nodul high malign risk normal thyroid parenchymamateri method materi use studi includ total thyroid ultrasound pattern obtain patient greec pattern repres fuse vector fuzzi featur ultrasound textur repres fuzzi local binari pattern wherea echogen repres fuzzi intens histogram encod thyroid ultrasound pattern discrimin support vector classifiersresult propos approach comprehens evalu use receiv oper characterist roc result show propos fusion scheme outperform previous thyroid ultrasound pattern represent method propos literatur best classif accuraci obtain polynomi kernel support vector machin reach estim area roc curveconclus fusion fuzzi local binari pattern fuzzi greylevel histogram featur effect state art approach represent thyroid ultrasound pattern can effect util detect nodul high malign risk context intellig medic system

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