4Arle Jeffrey E, Morriss C, Wang Z J, et al. Prediction of posterior fossa tumor type in children by means of magnetic resonance image properties, spectroscopy, and neural networks. J Neurosurg 1997;86:755 - 761
5Chabat F, Hansell DM, Yang G ZH. Computerized decision support in medical imaging (challenges in using image processing and automated feature extraction for improving diagnostic accuracy). Engineering in Medicine and Biology 2000; 5:89 ～ 96
6Poptani H, kaartinen J, Gupta RK, et al. Diagnostic assessment of brain tumors and non - neoplastic brain disorders in vivo using proton nuclear magnetic resonance spectroscopy and artificial neural networks. J Cancer Res Clin Oncol 1999; 125: 343 ～ 349
7Katsuragawa S, Doi K, MacMahon H, et al. Classification of normal and abnormal lungs with interstital diseases by rule - based method and artifical neural networks. J Digital Imag 1997; 10:108 ～ 114 Yoshida H, Masutani Y, MacEneaney P et al. Computerized
8Agrawal R, Mannila H, Srikant R, et al. Fast discovery of association rules(advances in knowledge discovery and data mining) .Morgan Kaufmann Usama M fayyad 1998; 25:307 - 328
9Robinson PJA. Radiology's Achilles' heel: error and variation in the interpretation of the rongen image. Br J Radiol 1997; 70:1085 - 1098
10DICOM CT, MR and CR images: Solving the pitfalls of vendorspecific DICOM implementations. J Digital Imaging 1998; 11:131 - 133
