Classification of benign and malignant thyroid nodules using wavelet texture analysis of sonograms

Objectives—The purpose of this study was to evaluate a computer-aided diagnostic
system using texture analysis to improve radiologic accuracy for identification of thyroid
nodules as malignant or benign.
Methods—The database comprised 26 benign and 34 malignant thyroid nodules.
Wavelet transform was applied to extract texture feature parameters as descriptors for each
selected region of interest in 3 normalization schemes (default, μ ± 3σ, and 1%–9%).
Linear discriminant analysis and nonlinear discriminant analysis were used for texture
analysis of the thyroid nodules. The first–nearest neighbor classifier was applied to features
resulting from linear discriminant analysis. Nonlinear discriminant analysis features were
classified by using an artificial neural network. Receiver operating characteristic curve
analysis was used to examine the performance of the texture analysis methods.
Results—Wavelet features under default normalization schemes from nonlinear
discriminant analysis indicated the best performance for classification of benign and malignant
thyroid nodules and showed 100% sensitivity, specificity, and accuracy; the area
under the receiver operating characteristic curve was 1.
Conclusions—Wavelet features have a high potential for effective differentiation of
benign from malignant thyroid nodules on sonography