Urmia University of Medical Sciences ; , Student Research Committee ; ,
Iranian Journal of Cancer PreventionJournal Country: Islamic Republic of Iran ISSN: 2008-2398 ISSN E: 2008-2401 Type of Publication: Journal Article Category: Humans, Male, Female, Type of Research: Clinical Keywords: Health Systems,Health and Biomedical Devices, Ultrasonography ,Thyroid Neoplasms ,Computer-Aided Design
The aim of this study was to evaluate computer aided diagnosis [CAD] system with texture analysis [TA] to improve radiologists' accuracy in identification of thyroid nodules as malignant or benign.
A total of 70 cases [26 benign and 44 malignant] were analyzed in this study. We extracted up to 270 statistical texture features as a descriptor for each selected region of interests [ROIs] in three normalization schemes [default, 3 [small sigma] and 1%-99%] . Then features by the lowest probability of classification error and average correlation coefficients [POE+ACC] , and Fisher coefficient [Fisher] eliminated to 10 best and most effective features. These features were analyzed under standard and nonstandard states. For TA of the thyroid nodules, Principle Component Analysis [PCA] , Linear Discriminant Analysis [LDA] and Non-Linear Discriminant Analysis [NDA] were applied. First Nearest-Neighbour [1-NN] classifier was performed for the features resulting from PCA and LDA. NDA features were classified by artificial neural network [A-NN] . Receiver operating characteristic [ROC] curve analysis was used for examining the performance of TA methods. The best results were driven in 1-99% normalization with features extracted by POE+ACC algorithm and analyzed by NDA with the area under the ROC curve [A [z] of 0.9722 which correspond to sensitivity of 94.45%, specificity of 100%, and accuracy of 97.14%. Our results indicate that TA is a reliable method, can provide useful information help radiologist in detection and classification of benign and malignant thyroid nodules
Ali Abbasian Ardakani ,Akbar Gharbali ,Afshin Mohammadi ,
Application of texture analysis method for classification of benign and malignant thyroid nodules in ultrasound images,
J. Taibah Univ. Med. Sci. 2015;
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