Automated storage and retrieval of thin-section CT images to assist diagnosis: System description and preliminary assessment

Radiology

Volumn 228, Issue 1, 2003, Pages 265-270

  Aisen, Alex M ^a   Broderick, Lynn S ^b   Winer Muram, Helen ^a   Brodley, Carla E ^c   Kak, Avinash C ^c   Pavlopoulou, Christina ^c   Dy, Jennifer ^c   Shyu, Chi Ren ^c   Marchiori, Alan ^c  

^a INDIANA UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b UNIVERSITY OF WISCONSIN MADISON   (United States)

^c PURDUE UNIVERSITY   (United States)

Author keywords

Computed tomography (CT), thinsection; Computers, diagnostic aid; Computers, educational aid; Lung, CT; Lung, diseases

Indexed keywords

ALGORITHM; ARTICLE; AUTOMATION; BRONCHIECTASIS; CLINICAL ARTICLE; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED TOMOGRAPHY; COMPUTER PROGRAM; DATA BASE; DIAGNOSTIC IMAGING; HUMAN; IMAGE ANALYSIS; LUNG EMPHYSEMA; LUNG FIBROSIS; MYCOBACTERIOSIS; PRIORITY JOURNAL; SARCOIDOSIS; VALIDATION PROCESS;

DATABASES, FACTUAL; DIAGNOSIS, COMPUTER-ASSISTED; INFORMATION STORAGE AND RETRIEVAL; RADIOLOGY INFORMATION SYSTEMS; SOFTWARE; TOMOGRAPHY, X-RAY COMPUTED; USER-COMPUTER INTERFACE;

EID: 0037600549     PISSN: 00338419     EISSN: None     Source Type: Journal    
DOI: 10.1148/radiol.2281020126     Document Type: Article

References (33)

1. Webb RW, Muller NL, Naidich DP. High resolution CT of the lung. 2nd ed. Philadelphia, Pa: Lippincott-Raven, 1996; 41.
2. Shyu C, Brodley CE, Kak A, Kosaka A, Aisen A, Broderick L. ASSERT, a physician-in-the-loop content-based image retrieval system for HRCT image databases. Computer Vision and Image Understanding 1999; 74:111-132.
3. Dy JG, Brodley CE, Kak AC, Aisen A, Broderick LS. The customized-queries approach to image retrieval. IEEE Transactions on Pattern Recognition and Machine Intelligence 2003; 25:373-378.
4. Brodley CE, Kak AC, Dy JG, Shyu CR, Alsen A, Broderick L. Content-based retrieval from medical image databases: a synergy of human interaction, machine learning and computer vision. In: Proceedings of the Sixteenth National Conference on Artificial Intelligence. Los Alamitos, Calif: IEEE Computer Society, 1999; 760-767.
5. Dy J, Brodley CE. Feature subset selection and order identification for unsupervised learning. In: The Seventeenth International Conference on Machine Learning. San Francisco, Calif: Morgan Kaufman, 2000; 247-254.
6. Kohavi R, John GH. Wrappers for feature subset selection. In: Artificial intelligence. Amsterdam, the Netherlands: Elsevier, 1997; 273-324.
7. Dy JG, Brodley CE, Kak A, Shyu C, Broderick LS. The customized-queries approach to CBIR using EM. In: IEEE Conference on Computer Vision and Pattern Recognition. Los Alamitos, Calif: IEEE Computer Society, 1999; 400-406.
8. Hosmer DW, Lemeshow S. Applied logistic regression. New York, NY: Wiley, 1989; 33.
9. Warren Burhenne LJ, Wood SA, D'Orsi CJ, et al. Potential contribution of computer-aided detection to the sensitivity of screening mammography. Radiology 2000; 215:554-562.
10. Freer TW, Ulissey MJ. Screening mammography with computer-aided detection: prospective study of 12,860 patients in a community breast center. Radiology 2001; 220:781-786.
11. Huo Z, Giger ML, Vyborny CJ, Wolverton DE, Schmidt RA, Doi K. Automated computerized classification of malignant and benign masses on digitized mammograms. Acad Radiol 1998; 5:155-168.
12. Rymon R, Zheng B, Chang YH, Gur D. Incorporation of a set enumeration trees-based classifier into a hybrid computer-assisted diagnosis scheme for mass detection. Acad Radiol 1998; 5:181-187.
13. Sahiner B, Chan HP, Petrick N, Helvie MA, Goodsitt MM. Computerized characterization of masses on mammograms: the rubber band straightening transform and texture analysis. Med Phys 1998; 25:516-526.
14. Buchbinder SS, Leichter IS, Bamberger PN, et al. Analysis of clustered microcalcifications by using a single numeric classifier extracted from mammographic digital images. Acad Radiol 1998; 5:779-784.
15. Thurfjell E, Thurfjell MG, Egge E, Bjurstam N. Sensitivity and specificity of computer-assisted breast cancer detection in mammography screening. Acta Radiol 1998; 39:384-388.
16. Mudigonda NR, Rangayyan RM, Desautels JE. Gradient and texture analysis for the classification of mammographic masses. IEEE Trans Med Imaging 2000; 19:1032-1043.
17. Rangayyan RM, Mudigonda NR, Desautels JE. Boundary modelling and shape analysis methods for classification of mammographic masses. Med Biol Eng Comput 2000; 38:487-496.
18. Nappi J, Dean PB. A multiscale algorithm for segmenting calcifications from high-resolution mammographic specimen radiographs. J Digit Imaging 2000; 13(2 suppl 1):130-132.
19. Jiang Y, Nishikawa RM, Wolverton DE, et al. Malignant and benign clustered microcalcifications: automated feature analysis and classification. Radiology 1996; 198:671-678.
20. Huo Z, Giger ML, Vyborny CJ, et al. Analysis of spiculation in the computerized classification of mammographic masses. Med Phys 1995; 22:1569-1579.
21. Monnier-Cholley L, MacMahon H, Katsuragawa S, Morishita J, Ishida T, Doi K. Computer-aided diagnosis for detection of interstitial opacities on chest radiographs. AJR Am J Roentgenol 1998; 171:1651-1656.
22. Kido S, Ikezoe J, Tamura S, Nakamura H, Kuroda C. A computerized analysis system in chest radiography: evaluation of interstitial lung abnormalities. J Digit Imaging 1997; 10:57-64.
23. Xu XW, Doi K, Kobayashi T, MacMahon H, Giger ML. Development of an improved CAD scheme for automated detection of lung nodules in digital chest images. Med Phys 1997; 24:1395-1403.
24. Giger ML, Bae KT, MacMahon H. Computerized detection of pulmonary nodules in computed tomography images. Invest Radiol 1994; 29:459-465.
25. Kanazawa K, Kawata Y, Niki N, et al. Computer-aided diagnosis for pulmonary nodules based on helical CT images. Comput Med Imaging Graph 1998; 22:157-167.
26. Archer DC, Coblentz CL, deKemp RA, Nahmias C, Norman G. Automated in vivo quantification of emphysema. Radiology 1993; 188:835-838.
27. Kauczor HU, Heitmann K, Heussel CP, Marwede D, Uthmann T, Thelen M. Automatic detection and quantification of ground-glass opacities on high-resolution CT using multiple neural networks: comparison with a density mask. AJR Am J Roentgenol 2000; 175:1329-1334.
28. Cherrak I, Paul JF, Jaulent MC, et al. Automatic stenosis detection and quantification in renal arteriography. In: Proceedings of the American Medical Informatics Association Fall Symposium 1997. Bethesda, Md: American Medical Informatics Association, 1997; 66-70.
29. Sarry L, Boire JY, Zanca M, Lusson JR, Cassagnes J. Assessment of stenosis severity using a novel method to estimate spatial and temporal variations of blood flow velocity in biplane coronary angiography. Phys Med Biol 1997; 42:1549-1564.
30. Chen EL, Chung PC, Chen CL, Tsai HM, Chang CI. An automatic diagnostic system for CT liver image classification. IEEE Trans Biomed Eng 1998; 45:783-794.
31. Summers RM, Beaulieu CF, Pusanik LM, Malley JD, Jeffrey RB Jr, Glazer DI, Napel S. Automated polyp detector for CT colonography: feasibility study. Radiology 2000; 216:284-290.
32. Korstjens CM, Geraets WG, van Ginkel FC, Prahl-Andersen B, van der Stelt PF, Burger EH. Longitudinal analysis of radiographic trabecular pattern by image processing. Bone 1995; 17:527-532.
33. Kalman BL, Reinus WR, Kwasny SC, Laine A, Kotner L. Prescreening entire mammograms for masses with artificial neural networks: preliminary results. Acad Radiol 1997; 4:405-414.