Document Detail

Cost-Effective and Non-Invasive Automated Benign and Malignant Thyroid Lesion Classification in 3D Contrast-Enhanced Ultrasound Using Combination of Wavelets and Textures: A Class of ThyroScan(tm) Algorithms.
MedLine Citation:
PMID:  21728394     Owner:  NLM     Status:  In-Data-Review    
Ultrasound has great potential to aid in the differential diagnosis of malignant and benign thyroid lesions, but interpretative pitfalls exist and the accuracy is still poor. To overcome these difficulties, we developed and analyzed a range of knowledge representation techniques, which are a class of ThyroScan(tm) algorithms from Global Biomedical Technologies Inc., California, USA, for automatic classification of benign and malignant thyroid lesions. The analysis is based on data obtained from twenty nodules (ten benign and ten malignant) taken from 3D contrast-enhanced ultrasound images. Fine needle aspiration biopsy and histology confirmed malignancy. Discrete Wavelet Transform (DWT) and texture algorithms are used to extract relevant features from the thyroid images. The resulting feature vectors are fed to three different classifiers: K-Nearest Neighbor (K-NN), Probabilistic Neural Network (PNN), and Decision Tree (DeTr). The performance of these classifiers is compared using Receiver Operating Characteristic (ROC) curves. Our results show that combination of DWT and texture features coupled with K-NN resulted in good performance measures with the area of under the ROC curve of 0.987, a classification accuracy of 98.9%, a sensitivity of 98%, and a specificity of 99.8%. Finally, we have proposed a novel integrated index called Thyroid Malignancy Index (TMI), which is made up of texture features, to diagnose benign or malignant nodules using just one index. We hope that this TMI will help clinicians in a more objective detection of benign and malignant thyroid lesions.
U R Acharya; O Faust; S V Sree; F Molinari; R Garberoglio; J S Suri
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Technology in cancer research & treatment     Volume:  10     ISSN:  1533-0338     ISO Abbreviation:  Technol. Cancer Res. Treat.     Publication Date:  2011 Aug 
Date Detail:
Created Date:  2011-07-06     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101140941     Medline TA:  Technol Cancer Res Treat     Country:  United States    
Other Details:
Languages:  eng     Pagination:  371-80     Citation Subset:  IM    
Dept. of ECE, Ngee Ann Polytechnic, Singapore 599489.
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