Abstract
A novel automated computerized scheme has been developed to assist radiologists for distinction between benign and malignant pulmonary nodules on radiographs using temporal subtraction images. Fifty-one chest radiographs including 26 malignant nodules and 25 benign nodules were used. The CAD system was developed based on features extracted from both chest radiographs and temporal subtraction images. The nodule was segmented automatically on both chest radiographs and subtraction images once the location of the nodule was indicated on the chest radiograph by a radiologist and/or computer. The nodule on the subtraction image was then segmented by thresholding with various pixel values, which were determined from the area of the histogram of pixel values on the temporal subtraction image. Twenty-three image features for each nodule were obtained from both subtraction images and current chest radiographs. The nodule image features included three morphological features obtained from the subtraction image and 10 gray-level features obtained from a histogram analysis of pixel values within the nodule on both subtraction and current images. A linear discrimination analysis (LDA) with six features was applied to determine the likelihood of pulmonary nodule malignancy. A receiver operating characteristic (ROC) analysis was used in the area under the ROC curve (Az) of the computer output obtained by use of the LDA. The six image features selected were the area, irregularity, mean, squared mean, and contrast obtained from the subtraction image and contrast obtained from the current image, which provided the highest Az value of the computer output obtained using the LDA. LDA was employed to separate benign from malignant nodules by use of a hyperplane. The output value of LDA represented the distance of either a benign or a malignant nodule from the hyperplane. In fact, the Az value of the computer output with six features obtained using the LDA for distinction between benign and malignant nodules was 0.851, which was obtained from a leave-oneout method. Our CAD system has the potential to assist radiologists in distinguishing between benign and malignant pulmonary nodules on chest radiographs using temporal subtraction images.
