@article{oai:kyutech.repo.nii.ac.jp:00000086,
 author = {Kim, Hyungseop and 金, 亨燮 and Maekado, Masaki and Tan, Joo kooi and タン, ジュークイ and Ishikawa, Seiji and 石川, 聖二 and Tsukuda, Masaaki},
 issue = {4},
 journal = {International Journal on Artificial Intelligence Tools},
 month = {Aug},
 note = {Medical imaging systems such as computed tomography, magnetic resonance imaging provided ahigh resolution image for powerful diagnostic tool in visual inspection fields by physician.Especially MDCT image can be used to obtain detailed images of the pulmonary anatomy, includingpulmonary diseases such as the pulmonary nodules, the pulmonary vein, etc. In the medical imageprocessing technique, segmentation is a difficult task because surrounding soft tissues and organshave similar CT values and sometimes contact with each other. We propose a new technique forautomatic segmentation of lung regions and its classification for ground-glass opacity from theextracted lung regions by computer based on a set of the thorax CT images. In this paper, wesegment the lung region for extraction of the region of interest employing binarization and labelingprocess from the inputted each slices images. The region having the largest area is regarded as thetentative lung regions. Furthermore, the ground-glass opacity is classified by correlation distributionon the slice to slice from the extracted lung region with respect to the thorax CT images. Experimentis performed employing twenty six thorax CT image sets and 96% of recognition rates wereachieved. Obtained results are shown along with a discussion},
 pages = {583--592},
 title = {Ground-Glass Opacity Detection by Using Correlation Between Successive Slice Images},
 volume = {16},
 year = {2007},
 yomi = {イシカワ, セイジ}
}