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흉부 CT의 컴퓨터보조 진단  

김종효 (서울대학교)
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1 Hu S, Hoffman EA, Reinhardt JM. Auto-matic lung segmentation for accurate quan-titation of volumetric X-ray CT images. IEEE Trans Med Imaging. 2001 Jun;20 (6)-.490-8
2 Takizawa H, Yamamoto S, Matsumoto T, et al. Recognition of lung nodules from X-ray CT images using 3D Markov random field models. Int Conf Pattern Rec 2002: 99-102
3 Xu N, Ahuja N, Bansal R. Automated lung nodule segmentation using dynamic Pro-gramming and EM-based classification. Proc SPIE 2002:4684:666-676
4 Giger ML, Bae KT, MacMahon H. Compu-terized detection of pulmonary nodules in CT images, Invest Radiol 1994:29:459-65
5 Jiang H, Yamamoto S, lisaku S, et al. Computer-aided diagnosis system for lung cancer screening by CT. In: Doi K, et al editors. Computer-aided diagnosis in med-ical imaging. Amsterdam, The Neterlands: Elsevier, 1999:125-30
6 Ukai Y, Niki N, Satoh H, et al. Computer aided diagnosis system for lung cancer based on retrospective helical CT image. Proc. SPIE 2000:3979:1028-39
7 Okumura T, Miwa, T, Kako J, YamamotoS, Matsumoto M, Tateno Y, et al. Variable N-Quoit filter applied for automatic de-tection of lung cancer by X-ray CT. In: Lemke HU, Vannier MW, Inamura K, Farman A, editors. Computer-assisted radiology. Amsterdam, The Netherlands: Elsevier Science, 1998:242-7
8 Lee Y, Hara T, Fujita H, Itoh S, Ishigaki T. Automated detection of pulmonary nod-ules in helical CT images based on an im-proved template-matching technique. IEEE Trans Med Imaging 2001:20:595-604
9 Brown MS, McNitt-Gray MF, Goldin JG, Suh RD, Sayre JW, Aberle DR. Patient-specific models for lung nodule de-tection and surveillance in CT images. IEEE Trans Med Imaging 2001:20:1242-50
10 Lee IJ, Gamsu G, Czum J. Wu N, Johnson R, Chakrapani S. Lung Nodule Detection on Chest CT: Evaluation of a Computer Aided Detection (CAD) System. Korean J Radiol. 2005 Apr-Jun;6(2):89-93
11 Henschke CI, Yankelevitz DF, Mirtcheva R, et al. CT screening for lung cancer: fre-quency and signiflcance of part-solid and nonsolid nodules. AJR Am J Roentgenol. 2002 May; 178(5): 1053-7
12 Sone S, Takashima S, Li F, et al. Mass screening for lung cancer with mobile Spi-ral computed tomography scanner. Lancet 998:351:1242-5
13 Diederich S, Wormanns D, Semik M, Thomas M, Lenzen H, Roos N, et al. Screening for early lung cancer wlth low-dose spiral CT: prevalence in 817 asymptomatic smokers. Radiology 2002; 222:773-81
14 Kawata Y, Niki N, Ohmatsu H, Moriyama N. Example-based assisting approach for pulmonary nodule classification in three-dimensional thoracic computed tomography images. Acad Radiol. 2003:10(12): 1402-15
15 Zhao B, Yankelevitz D, Reeves A, Henschke C. Two-dimensional multi-crite-rion segmentation of pulmonary nodules on helical CT-images. Med Phys 1999:26: 889-95
16 Wormanns D, Kohl G, Klotz E, Heindel W, Diederich S. Clinical evaluation of the re-producibility of volume measurements of pulmonary nodules. In: Proceedings of SPIE Medical Imaging 2002. SPIE 2002; 4684:316-22
17 Fan L, Qian J, Odry B, Shen H, Naidich D, Kohl G, et al. Automatic segmentation of pulmonary nodules by using dynamic 3D cross-correlation for interactive CAD systems. In: Proceedings of SPIE Medical Imaeing 2002. SPIE 2002:4684: 1362-9
18 Yankelevitz DF, Reeves AP, Kostis WJ, Zhao B, Henschke CI. Small pulmonary nodules: volumetrically determined growth rates based on CT evaluation. Radiology. 2000 Oct:217(1):251-6
19 Wormanns D, Kohl G, Klotz E, Marheme A, Beyer F, Heindel W, Diederich S. Volumetric measurements of pulmonary nodules at multi-row detector CT: in vivo reprodudbility. Eur Radiol. 2004 Jan;14 (l):86-92. Epub 2003 Nov 13
20 Kostis W, Reeves A, Yankelewitz D, Henschke C. Three-dimensional Segmenta-tion of solitary pulmonary nodules from helical CT scans. In: Proceedings of Computer Assisted Radiology and Surgery CARS 1999. Amsterdam, The Netherlands: Elsevier, 1999:203-7
21 Arimura H, Katsuragawa S, Suzuki K, Li F, Shiraishi J, Doi K. Computerized scheme for automated detection of lung nodules in low-dose CT images for lung cancer screening. Acad Radiol 2004:11: 617-29
22 Kaneko M, Eguchi K, Ohmatsu H, et al. Peripheral lung cancer: screening and de-tection with low-dose spiral CT versus radiography. Radiology 1996:201:798-802
23 McNitt-Gray MF, Hart EM, Wyckoff N, Sayre JW, Goldin JG, Aberle DR. A pat-tern classiflcation approach to characterizing solitary pulmonary nodules imaged on high resolution CT: preliminary results. Med Phys. 1999 Jun: 26(6): 880-8
24 Kim KG, Goo JM, Kim JH, Lee HJ, et al. Computer-aided detection of localized ground-glass opacity in the lung on CT images. Radiology in press
25 Goo JM, Tongdee T, Tongdee R, Yeo K, Hildebolt CF, Bae KT. Volumetric measurement of synthetic lung nodules wlthmulti-detector row CT: effect of variousimage reconstruction parameters and Seg-mentation thresholds on measurement ac-curacy. Radiology. 2005 Jun; 235(3): 850-6
26 Doi K, MacMahon H, et al. Computer-aid-ed diagnosis in radiology : potential and pitfalls. Eur J radiol 1999:31:97-109
27 Swensen SJ, Jett JR, Sloan JA, Midthun DE, Hartman TE, Sykes AM, et al. Screening for lung cancer with low-dose spiral computed tomography. Am J Respir Crit Care Med 2002:165:508-13
28 Li F, Aoyama M, Shiraishi J, Abe H, Li Q, Suzuki K, et al. Improvement in radiol ogists' performance for differentiating small benign from malignant lung nodules on high-resolution CT by using computer estimated likelihood of malignancy. AJR 2004:183:1209-15
29 Henschke CI, McCauley DI, Yankelevitz DF, et al. Early lung cancer action proj ect: overall design and findings from base-line screening. Lancet 1999:354:99-105
30 Kido S, Kuriyama K, Higashiyama M, Kasugai T, Kuroda C. Fractal analysis of internal and peripheral textures of small peripheral bronchoeeruc carcinomas in thin-section computed tomography: Com-parison of bronchioloalveolar cell Carcino-mas with nonbronchioloalveolar cell Carcinomas. J Comput Assist Tomogr. 2003 Jan-Feb;27(1):56-61
31 Lee JW, Goo JM, Lee HJ, Kim JH, Kim S, Kim YT. The potential contribution of a computer-aided detection system for lung nodule detection in multi-detector row computed tomography. Invest Radiol 2004:39:649-55
32 Goo JM, Lee JW, et al. Automatic lung nodule detection at low-dose CT: prelimi nary expehence. Koran J radiol 2003:4: 211-216
33 Boscolo R, Brown MS, McNitt-Gray MF. Medical Image Segmentation wlth Know ledgeguided Robust Active Contours. Radio-graphics. 2002:22:437-448
34 Armato III SG, Giger ML, Moran CJ, et al. Computerized detection of pulmonary nod-ules on CT scans. Radiographics 1999:19: 1303-11.Lawler LP, Wood SA, Paunu HS, Fishman EK. Computer-assisted detection of pulmonary nodules: preliminary Ob-servations using a prototype system with multi detector-row CT data sets. J DigitImaging 2003; 16:251-61
35 Kanazawa K, Kawata Y, Niki N, Satoh H, Ohmatsu H, Kakinuma R, et al. Computer aided diagnostic system for pulmonary nodules based on helical CT images. In: Doi K, MacMahon H, Giger ML, Hoffmann KR, editors. Computer-aided diagnosis in medical imaging. Amsterdam, The Nether-lands: Elsevier Science, 1999:131-6
36 Brown MS, Goldin JG, Suh RD, et al. Lung micronodules: automated method for detection at thin-section CT-initial experi-ence. Radiology 2003:226:256-62
37 Armato SG III, Giger ML, MacMahon H. Automated detection of lung nodules in CT scans: preliminary results. Med Phys 2001-,28:1552-61
38 Li F, Sone S, Abe H, MacMahon H, Armato SG 3rd, Doi K. Lung cancers missed at low-dose helical CT screening in a general population: comparison of Clin-ical, histopathologic, and imaging findings. Radiology. 2002:225(3):673-83
39 Gurcan MN, Sahiner B, Petrick N, et al. Lung nodule detection on thoracic Com-puted tomography images: Preliminary evaluation of a computer-aided diagnosis system. Med Phys 2002;29:2552-8
40 Aoyama M, Li Q, Katsuragawa S, Li F, Sone S, Doi K. Computerized scheme for determi-nation of the likelihood measure of malig-nancy for pulmonary nodules on low-dose CT images. Med Phys 2003:30: 387-94
41 Armato SG III, Li F, Giger ML, MacMahon H, Sone S, Doi K. Performance of auto-mated CT nodule detection on missed Can-cers from a lung cancer screening program.Radiology 2002:225:685-92
42 Wormanns D, Fiebich M, Saidi M, et al. Automatic detection of pulmonary nodules at spiral CT: clinical application of a Com-Puter-aided diagnosis system. Eur Radiol 2002; 12:1052-7
43 Brown MS, Goldin JG, Rogers S, Kim HJ, et al. Computer-aided lung nodule de-tection in CT: results of large-scale Ob-server test. Acad Radiol. 2005 Jun;12(6): 681-6
44 Wiemker R, Rogalla P, Hein E, Blaffert TRosch P. Computer aided segmentation of pulmonary nodules: automated vascula ture cutoff in thick- and thinslice CT. In: Proceedings of Computer Assisted Radio-logy and Surgery, CARS 2003. Amster-dam, The Netherlands: Elsevier, 2003: 965-790
45 Wiemker R, Zwartkruis A. Optimal thresholding for 3D segmentation of Pul-monary nodules in high resolution CT. In: Proceedings of International Conference on Computer Assisted Radiology and Surgery CARS 2001. Amsterdam, The Netherlands: Elsevier Publishers, 2001:653-8
46 Suzuki K, Armato III SG, Li F, et al. Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose CT. Med Phys 2003:30:1602-17
47 Doi K. Current status and future potential of computer-aided diagnosis in medical imaging. Br J Radiol 2005:78:83-819