Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/97356
Title: ThyroScreen system : high resolution ultrasound thyroid image characterization into benign and malignant classes using novel combination of texture and discrete wavelet transform
Authors: Acharya, U. Rajendra
Faust, Oliver
Sree, Subbhuraam Vinitha
Molinari, Filippo
Suri, Jasjit S.
Issue Date: 2012
Source: Acharya, U. R., Faust, O., Sree, S. V., Molinari, F., & Suri, J. S. (2012). ThyroScreen system : high resolution ultrasound thyroid image characterization into benign and malignant classes using novel combination of texture and discrete wavelet transform. Computer methods and programs in biomedicine, 107(2), 233-241.
Series/Report no.: Computer methods and programs in biomedicine
Abstract: 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.
URI: https://hdl.handle.net/10356/97356
http://hdl.handle.net/10220/13145
ISSN: 0169-2607
DOI: 10.1016/j.cmpb.2011.10.001
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:MAE Journal Articles

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