Classification of breast tumors using sonographic texture analysis

The purpose of this study was to evaluate a computer-aided diagnostic
system with texture analysis to improve radiologists’ accuracy in identification of breast
tumors as malignant or benign.
Methods—The database included 20 benign and 12 malignant tumors. We extracted 300
statistical texture features as descriptors for each selected region of interest in 3 normalization
schemes (default, μ – 3σ, and μ + 3σ, where μ and σ were the mean value and
standard deviation, respectively, of the gray-level intensity and 1%–99%). Then features
determined by the Fisher coefficient and the lowest probability of classification error +
average correlation coefficient yielded the 10 best and most effective features. We analyzed
these features under 2 standardization states (standard and nonstandard). For texture
analysis of the breast tumors, we applied principle component, linear discriminant, and
nonlinear discriminant analyses. First–nearest neighbor classification was performed for
the features resulting from the principle component and linear discriminant analyses.
Nonlinear discriminant analysis features were classified by an artificial neural network.
Receiver operating characteristic curve analysis was used for examining the performance
of the texture analysis methods.
Results—Standard feature parameters extracted by the Fisher coefficient under the default
and 3σ normalization schemes via nonlinear discriminant analysis showed high performancefor
discrimination between benign and malignant tumors, with sensitivity of 94.28%,
specificity of 100%, accuracy of 97.80%, and an area under the receiver operating characteristic
curve of 0.9714.
Conclusions—Texture analysis is a reliable method and has the potential to be used
effectively for classification of benign and malignant tumors on breast sonography.