Comput Methods Programs Biomed - ThyroScreen system: high resolution ultrasound thyroid image characterization into benign and malignant classes using novel combination of texture and discrete wavelet transform.

Tópicos

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Resumo

Using right equipment and well trained personnel, ultrasound of the neck can detect a large number of non-palpable thyroid nodules. However, this technique often suffers from subjective interpretations and poor accuracy in the differential diagnosis of malignant and benign thyroid lesions. Therefore, we developed an automated identification system based on knowledge representation techniques for characterizing the intra-nodular vascularization of thyroid lesions. Twenty nodules (10 benign and 10 malignant), taken from 3-D high resolution ultrasound (HRUS) images were used for this work. Malignancy was confirmed using fine needle aspiration biopsy and subsequent histological studies. A combination of discrete wavelet transformation (DWT) and texture algorithms were used to extract relevant features from the thyroid images. These features were fed to different configurations of AdaBoost classifier. The performance of these configurations was compared using receiver operating characteristic (ROC) curves. Our results show that the combination of texture features and DWT features presented an accuracy value higher than that reported in the literature. Among the different classifier setups, the perceptron based AdaBoost yielded very good result and the area under the ROC curve was 1 and classification accuracy, sensitivity and specificity were 100%. Finally, we have composed an Integrated Index called thyroid malignancy index (TMI) made up of these DWT and texture features, to facilitate distinguishing and diagnosing benign or malignant nodules using just one index or number. This index would help the clinicians in more quantitative assessment of the thyroid nodules.

Resumo Limpo

use right equip well train personnel ultrasound neck can detect larg number nonpalp thyroid nodul howev techniqu often suffer subject interpret poor accuraci differenti diagnosi malign benign thyroid lesion therefor develop autom identif system base knowledg represent techniqu character intranodular vascular thyroid lesion twenti nodul benign malign taken d high resolut ultrasound hrus imag use work malign confirm use fine needl aspir biopsi subsequ histolog studi combin discret wavelet transform dwt textur algorithm use extract relev featur thyroid imag featur fed differ configur adaboost classifi perform configur compar use receiv oper characterist roc curv result show combin textur featur dwt featur present accuraci valu higher report literatur among differ classifi setup perceptron base adaboost yield good result area roc curv classif accuraci sensit specif final compos integr index call thyroid malign index tmi made dwt textur featur facilit distinguish diagnos benign malign nodul use just one index number index help clinician quantit assess thyroid nodul

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