A comparative study for 2D and 3D computer-aided diagnosis methods for solitary pulmonary nodules

Computerized Medical Imaging and Graphics

Volumn 32, Issue 4, 2008, Pages 270-276

  Yeh, Chinson ^a   Wang, Jen Feng ^a   Wu, Ming Ting ^b,c   Yen, Chen Wen ^a   Nagurka, Mark L ^d   Lin, Chen Liang ^a  

^a NATIONAL SUN YAT SEN UNIVERSITY   (Taiwan)

^b KAOHSIUNG VETERANS GENERAL HOSPITAL   (Taiwan)

^c NATIONAL YANG MING UNIVERSITY   (Taiwan)

^d MARQUETTE UNIVERSITY   (United States)

Author keywords

Computed tomography; Computer aided diagnosis; Contrast enhancement; Likelihood of malignancy; Lung cancer; Lung nodule; Solitary pulmonary nodule

Indexed keywords

COMPUTATIONAL METHODS; COMPUTER AIDED DIAGNOSIS; COMPUTERIZED TOMOGRAPHY; PULMONARY DISEASES;

CONTRAST ENHANCEMENT; LIKELIHOOD OF MALIGNANCY; LUNG CANCER; LUNG NODULE; SOLITARY PULMONARY NODULE;

TUMORS;

ARTICLE; COMPARATIVE STUDY; COMPUTER ASSISTED DIAGNOSIS; CONTRAST ENHANCEMENT; DIAGNOSTIC ACCURACY; LUNG NODULE; PERFORMANCE; PRIORITY JOURNAL; STATISTICAL SIGNIFICANCE; THREE DIMENSIONAL IMAGING;

ADULT; AGED; AGED, 80 AND OVER; ALGORITHMS; COIN LESION, PULMONARY; CONTRAST MEDIA; DIAGNOSIS, COMPUTER-ASSISTED; FEMALE; HUMANS; IMAGE ENHANCEMENT; IMAGING, THREE-DIMENSIONAL; LUNG NEOPLASMS; MALE; MIDDLE AGED; NEURAL NETWORKS (COMPUTER); TOMOGRAPHY, X-RAY COMPUTED;

EID: 41649114516     PISSN: 08956111     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compmedimag.2008.01.003     Document Type: Article

References (39)

1. Jemal A., Murray T., Ward E., Samuels A., Tiwari R.C., Ghafoor A., et al. Cancer statistics 2005. Cancer J Clin 55 (2005) 10-30
2. Winer-Muram H.T. The solitary pulmonary nodule. Radiology 239 1 (2006) 34-49
3. Tan B.B., Flaherty K.R., Kazerooni E.A., and Iannettoni M.D. The solitary pulmonary nodule. Chest 123 1 (2003) 89-96
4. Shah S.K., McNitt-Gray M.F., De Zoysa K.R., Sayre J.W., Kim H.J., Batra P., et al. Solitary pulmonary nodule diagnosis on CT: results of an observer study. Acad Radiol 12 4 (2005) 496-501
5. McCarville M.B., Lederman H.M., Santana V.M., Daw N.C., Shochat S.J., Li C.S., et al. Distinguishing benign from malignant pulmonary nodules with helical chest CT in children with malignant solid tumors. Radiology 239 2 (2006) 514-520
6. McNitt-Gray M. Lung nodules and beyond: approaches, challenges and opportunities in thoracic CAD. Int Congr Ser 1268 (2004) 896-901
7. Li Q., Li F., Suzuki K., Shiraishi J., Abe H., Engelmann R., et al. Computer-aided diagnosis in thoracic CT. Semin Ultrasound CT MR 25 5 (2005) 357-363
8. McNitt-Gray M.F., Wyckoff N., Sayre J.W., Goldin J.G., and Aberle D.R. The effects of co-occurrence matrix based texture parameters on the classification of solitary pulmonary nodules imaged on computed tomography. Comput Med Imaging Graph 23 6 (1999) 339-348
9. Yi C.A., Lee K.S., Kim E.A., Han J., Kim H., Kwon O.J., et al. Solitary pulmonary nodules: dynamic enhanced multi-detector row CT study and comparison with vascular endothelial growth factor and microvessel density. Radiology 233 1 (2004) 191-199
10. Jeong Y.J., Lee K.S., Jeong S.Y., Chung M.J., Shim S.S., Kim H.., et al. Solitary pulmonary nodule: characterization with combined wash-in and washout features at dynamic multi-detector row CT. Radiology 237 2 (2005) 675-683
11. Suzuki K., Li F., Sone S., and Doi K. Computer-aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low-dose CT by use of massive training artificial neural network. IEEE Trans Med Imag 24 9 (2005) 1138-1150
12. Fuchs TOJ, Kachelriess M, Kalender WA, Fast volume scanning approaches by X-ray-computed tomography. In: Proc IEEE Comput Soc Bioinform Conf; 2003;91(10). p. 1492-502.
13. Kanazawa K., Kawata Y., Niki N., Satoh H., Ohmatsu H., Kakinuma R., et al. Computer-aided diagnosis for pulmonary nodules based on helical CT images. Comput Med Imaging Graph 22 2 (1998) 157-167
14. Kawata Y., Niki N., Ohmatsu H., and Moriyama N. Example-based assisting approach for pulmonary nodule classification in three-dimensional thoracic computed tomography images. Acad Radiol 10 12 (2003) 1402-1415
15. Awai K., Murao K., Ozawa A., Nakayama Y., Nakaura T., Liu D., et al. Pulmonary nodules: estimation of malignancy at thin-section helical CT-effect of computer-aided diagnosis on performance of radiologists. Radiology 239 1 (2006) 276-284
16. Mori K., Niki N., Kondo T., Kamiyama Y., Kodama T., Kawada Y., et al. Development of a novel computer-aided diagnosis system for automatic discrimination of malignant from benign solitary pulmonary nodules on thin-section dynamic computed tomography. J Comput Assist Tomogr 29 2 (2005) 215-222
17. Shah S.K., McNitt-Gray M.F., Rogers S.R., Goldin J.G., Suh R.D., Sayre J.W., et al. Computer aided characterization of the solitary pulmonary nodule using volumetric and contrast enhancement features. Acad Radiol 12 10 (2005) 1310-1319
18. Petkovska I., Shah S.K., McNitt-Gray M.F., Goldin J.G., Brown M.S., Kim H.J., et al. Pulmonary nodule characterization: a comparison of conventional with quantitative and visual semi-quantitative analyses using contrast enhancement maps. Eur J Radiol 59 2 (2006) 244-252
19. Yeh C, Lin C-L, Wu M-T, Yen C-W, Wang J-F. A neural network based diagnosis method for characterizing solitary pulmonary nodules. Neurocomputing 2008, doi:10.1016/j.neucom.2007.11.009.
20. Hu S., Hoffman E.A., and Reinhardt J.M. Automatic lung segmentation for accurate quantitation of volumetric x ray CT images. IEEE Trans Med Imag 20 6 (2001) 490-498
21. Armato SG 3rd, MacMahon H. Automated lung segmentation and computer-aided diagnosis for thoracic CT scans. Int Congr Ser 2003; 1256:977-82.
22. Kuhnigk J.M., Dicken V., Bornemann L., Bakai A., Wormanns D., Krass S., et al. Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic CT scans. IEEE Trans Med Imag 25 5 (2006) 417-434
23. Reeves A.P., Chan A.B., Yankelevitz D.F., Henschke C.I., Kressler B., and Kostis W.J. On measuring the change in size of pulmonary nodules. IEEE Trans Med Imag 25 4 (2006) 435-450
24. Reeves A.P., and Kostis W.J. Computer-aided diagnosis for lung cancer. Radiol Clin North Am 38 3 (2000) 497-509
25. Folkman J., Merler E., Abernathy C., and Williams G. Isolation of a tumor factor responsible for angiogenesis. J Exp Med 133 2 (1971) 275-288
26. Miles K.A. Tumour angiogenesis and its relation to contrast enhancement on computed tomography: a review. Eur J Radiol 30 3 (1999) 198-205
27. Miles K.A. Perfusion CT for the assessment of tumor vascularity: which protocol?. Br J Radiol 76 1 (2003) 36-42
28. Swensen S.J., Viggiano R.W., Midthun D.E., Muller N.L., Sherrick A., Yamashita K., et al. Lung nodule enhancement at CT: multicenter study. Radiology 214 1 (2000) 73-80
29. Chang YC, Cheng HH, Yeh CF, Lee WJ, Yang PC, Liu MH, et al. Textural analysis of lung nodule smaller than 3 cm with dynamic contrast enhancement. In: Proc 1st World Congress of Thoracic Imaging and Diagnosis in Chest Disease. 2005.
30. Gaeta M., Volta S., Bartiromo G., Stroscio S., Minutoli F., and Barone M. Contrast-enhanced study of solitary pulmonary nodules with thin-section computed tomography. Radiol Med (Torino) 94 3 (1997) 189-192
31. Li F., Sone S., Abe H., MacMahon H., and Doi K. Malignant versus benign nodules at CT screening for lung cancer: comparison of thin-section CT findings. Radiology 233 3 (2004) 793-798
32. Sadjadi F.A., and Hall E.L. Three-dimensional moment invariants. IEEE Trans Pattern Anal Machine Intell 2 2 (1980) 127-136
33. Mangin J.F., Poupon F., Duchesnay E., Rivie're D., Cachia A., Collins D.L., et al. Brain morphometry using 3D moment invariants. Med Imag Anal 8 3 (2004) 187-196
34. Ng B, Abugharbieh R, Huang X, McKeown MJ. Characterizing fMRI activations within regions of interest (ROIs) using 3D moment invariants. In: Proc IEEE 2006 Conf Comput Vision Patt Recog (CVPR). 2006.
35. Haykin S. Neural network: a comprehensive foundation, 2nd ed. Upper Saddle River, NJ: Prentice Hall; 1999.
36. Breiman L. Bagging predictors. Mach Learn 24 2 (1996) 123-140
37. Dietterich T.G. An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Mach Learn 40 2 (2000) 139-157
38. Opitz D, Maclin R. An empirical evaluation of bagging and boosting for artificial neural networks. In: Conf Rec 1997 IEEE Int Conf Neural Network. 1997.
39. Bishop C.M. Neural Networks for Pattern Recognition (1995), Oxford: Oxford University Press