A New CAD System for Detecting Localized Ground Glass Opacity Nodules in Lung CT images Using Cross and Coronary Section Images

This study is intended to propose a novel Computer Aided Diagnosis (CAD) scheme for automatic detection of localized Ground Glass Opacity (GGO) nodules in chest Computed Tomography (CT) images. The main idea of our method is to confirm the existence of the GGO candidates in the cross sectional CT images by examining the coronary sectional CT images based on the fact that nodular candidates tend to appear circular in both sections. Our detection scheme begins with a preprocessing stage to the cross sectional CT image to extract the lung region and enhance the intensity values of the nodular regions. We then filter the resulting image with Gabor filter followed by thresholding and labeling to assign the suspected regions and match them with some predefined reference Gaussian templates. Thereafter, some characteristic morphological and gray level features are used to discriminate the potential GGO candidates. Finally, we confirm the existence of the GGO nodules by examining the coronary sectional CT images. Receiver Operating Characteristic (ROC) curve for our CAD scheme was constructed to evaluate its performance. The proposed scheme had an area under the ROC curve of 0.94, which proves its potential effectiveness in GGO nodule detection