This paper presents a method for detecting suspicious nodules based on successive low-dose helical CT images. The method uses both initial and follow-up images to improve nodule detection performance. The basic idea of the detection is to register nodule images measured at different time and to assess the changes in size, shape, and density of the nodule. Since there are several variations of nodule changes, such as stable, shrinking, expansion in size, disappearance, appearance, and separation, a coarse-to-fine registration technique was adopted to deal with large nodule deformation. Especially, the fine registration is performed by excluding nodule regions and using nodule surroundings to avoid effects of nodule deformations in alignment task. In a preliminary experiment, the method was applied to ten cases with successive scans. From visual inspection, the corresponding results between initial and follow-up images were acceptable in clinical use. More researches using a large data set will be required. Still, we believe that the method has the potential of detecting suspicious nodules for use in a computer-aide diagnosis system.
The lung cancer is very difficult to treat when condition of disease reaches an advanced stage. Therefore, the early detection and the early treatment by the mass cscreening are important. Now, the3 mass screening using the chest X-rays film is performed, and its detection rate is low. Recently, mass screening for lung cancer started using helical CT. However, since each subject has about 30 images, there is concern about the increase ofa burden to a physician. This comparative reading system solves difficulties of efficient display with the past and present images. But, automatic slice-image-matching is difficult by computer, since the states of the lungs at the time of photography differ from each other. This research analyzed change of the lungs between images with time and proposed automatic slice image mtching algorithm for comparative reading.
The objective of our study is to develop a new computer- aided diagnosis (CAD) system to support effectually the comparative reading using serial helical CT images for lung cancer screening without using the film display. The placement of pulmonary shadows between the serial helical CT images is sometimes different to change the size and the shape of lung by inspired air. We analyzed the motion of the pulmonary structure using the serial cases of 17 pairs, which are different in the inspired air. This algorithm consists of the extraction process of region of interest such as the lung, heart and blood vessels region using thresholding and fuzzy c-means method, and the comparison process of each region in serial CT images using template matching. We validated the efficiency of this algorithm by application to image of 60 subjects. The algorithm could compare the slice images correctly in most combinations with respect to physician's point of view. The experimental results of the proposed algorithm indicate that our CAD system without using the film display is useful to increase the efficiency of the mass screening process.