1Doi K. Current status and future potential of computer-aided diagnosis in medical imaging [ J]. British Journal of Radiology, 2005, 78 (Special Issue) : S3-S19.
2Lee Yongbum, Ham T. Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique [J]. IEEE Trans Med Imaging, 2001, 20(7) : 595-604.
3Jane P Ko, Margrit Betke. Chest CT: automated nodule detection and assessment of change over time-preliminary experiencel [J]. Radiology, 2001, 218( 1 ) :267-273.
4Mousa W A H, Khan M A U. Lung nodule classification utilizing support vectormachines [ C]. Proc. Int. Conf. Image Processing 2002, 3 : 153-156.
5Suzuki K, Doi K. How can a massive training artificial neural network (MTANN) be trained with a small number of cases in the distinction between nodules and vessels in thoracic CT[J]. Acad Radiol, 2005, 12(10) :1333-1341.
6Lin Daw-tung, Yah Chung-ren, Chen Wen-tai. Autonomous detection of pulmonary nodules on CT images with a neural network- based fuzzy system. [ J ]. Computerized Medical Imaging and Graphics, 2005, 29(6):447-458.
7Chen Jing-di, Shen Bing-qi. Imaging diagnose and differentiating of solitary pulmonary nodules[J]. New Chinese Medicine, 2005, 36(10) :607-609.
8Tang A W K, Moss H A, Robertson R J H. The solitary pulmonary nodule [ J ]. European Journal of Radiology, 2003, 45 (1) :69- 77.
9Otsu N. A threshold selection method from grey-level histogram [J], IEEE Trans System Man Cybemet, 1978, 9(1 ) :62-66.
10Guo Ping, Wang Yan-xia, Chen Qi-xin, et al. A method for rebuilding graph boundary based on fractal [ J]. Journal of Computational Information Systems, 2006, 2(4) : 1427-1432.
