Using CT images from the National Lung Screening Trial (NLST) of the National Cancer Institute (NCI), interpreted by radiologists at the Georgetown University, our goal was to investigate the feature extraction method using discrete wavelet transform (DWT) and to demonstrate their potential in distinguishing between benign and malignant nodule status. We analyzed multiple 2 mm thick slices of 40 subjects with benign nodules and 7 subjects with malignant
nodules for a total of 112 and 78 slices, respectively. Data was analyzed in the region-of-interest (ROI) that included nodule and surrounding areas in three different-sized windows. A linear discriminant analysis (LDA) of wavelets coefficients was used for data analysis. In particular we examined discriminative power of the wavelet based features using Fisher LDA, and evaluated the classification results using decision matrix (DM) for matched sample (MS). For visualization we used 3-D Heat Maps, originally developed in MATLAB(R) (MathWorks, Natick, MA) for gene expression array analysis, modified to display the magnitude of similarities between cases under analysis. The use of DWT in the image pre-processing modules resulted in a significant improvement in discrimination between benign and malignant nodules. The results show better classification accuracy with the DWT based features, as compared to
previously proposed classification features (p-values: 0.008, 0.022, and 0.039, depending on window size). The Heat Maps provide useful data visualization for further investigation as they have the ability to identify cases that should be further explored to understand why some of the benign nodules look similar to malignant in the wavelet domain.