Multifeature analysis of Gd-enhanced MR images of breast lesions.

TLDR: In this paper, linear discriminant analysis of different independent features of MR images of breast lesions was applied to find the best combination of features yielding the highest classification accuracy, and three independent classes of features, including characteristics of Gd-DTPA-uptake curve, boundary, and texture were evaluated.

Abstract: The objective of this study was to determine whether linear discriminant analysis of different independent features of MR images of breast lesions can increase the sensitivity and specificity of this technique. For MR images of 23 benign and 20 malignant breast lesions, three independent classes of features, including characteristics of Gd-DTPA-uptake curve, boundary, and texture were evaluated. The three classes included five, four and eight features each, respectively. Discriminant analysis was applied both within and across the three classes, to find the best combination of features yielding the highest classification accuracy. The highest specificity and sensitivity of the different classes considered independently were as follows: Gd-up-take curves, 83% and 70%; boundary features, 86% and 70%; and texture, 70% and 75%, respectively. A combination of one feature each from the first two classes and age yielded a specificity of 79% and sensitivity of 90%, whereas highest figures of 93% and 95%, respectively, were obtained when a total of 10 features were combined across different classes. Statistical analysis of different independent classes of features in MR images of breast lesions can improve the classification accuracy of this technique significantly.