Detection of Lung Regions from LDCT Images and 3D Image Registration Using FFD

In recent years, lung cancer is a serious disease worldwide. To overcome the disease, early detection and early treatment of lung cancer are important tasks. In Japan, CT (Computed Tomography) examination is performed in many medical facilities for visual screening. However, there is a problem that huge number of images taken by CT is a burden to the doctor. Therefore, the CAD (Computer Aided Diagnosis) system is in the spotlight to reduce the burden. On the other hand, in CT screening, LDCT (Low Dose Computed Tomography) is desirable considering radiation exposure. However, the LDCT image is characterized by lower image quality at lower dose. Therefore, a CAD that can be applied to LDCT is needed. In this paper, we propose a lung field detection method and a three-dimensional (3D) registration method to generate temporal subtraction images that can be applied to LDCT images. Our method consists of the following steps: detection of lung regions using an active contour model, correction of corresponding slices between images, global matching based on the center of gravity, setting the VOI (volume of interest), and local matching on the VOI. In this paper, we apply our method to LDCT images of 7 cases. In addition, we conducted a comparison experiment with other registration methods. In the proposed method, a decrease of 7.55% in FWHM and 34.4% in sum of histogram of temporal subtraction images compared to the other method, and its usefulness was verified.