Automatic identification of lung candidate nodules on chest CT images based on temporal subtraction images

Lung cancer is the most common cause of death from cancer worldwide. Therefore, for the purpose of early detection of cancer, mass screening and thorough examination have been carried out. Lung cancer is detected easily by using chest MDCT (Multi Detector-row Computed Tomography) images. However, radiologists are apprehended burden by many chest MDCT images which are required interpretation of radiograms. So the CAD (Computer Aided Diagnosis) systems that could relieve radiologist´s stress and diagnose accuracy could be improved are expected. One of the CAD systems, temporal subtraction technique that emphasized time-dependent change is reported. This technique is used for diagnosis assistance of detected candidate nodules from CT images. In this paper, the candidate nodules under 20[mm] are extracted from temporal subtraction images. We highlighted the candidate nodules based on features analysis of images. We applied proposed method to 31 cases of chest MDCT images in which the number of nodules was more than one. We got a result of TPR:96.9[%], FPR:6.45[/case].