Improving the conspicuity of lung nodules by use of "Virtual Dual-Energy" radiography

In the field of medical image processing, computer programs have been developed and approved for use in clinical practice that aid radiologists in detecting the abnormalities on radiology exams. In this study, a Computer-aided detection (CADe) scheme with improved sensitivity and specificity is developed. Chest radiograph(CXR) images are used as the input, which is then segmented using Multi segment active shape model (M-ASM). Massive Training Artificial Neural Network(MTANN) is used to suppress the ribs and clavicles as a result of which, Virtual Dual-Energy(VDE) image is developed. In addition, an Hop-Field Neural Network(HNN) is used to improve the rib contrast. Features are extracted from the original image and the VDE image. A nonlinear support vector machine(SVM)classifier was employed for classification of the nodule candidates and a linear discrimination analysis is used to detect the nodules.