Image analysis – Applications – Biomedical applications
Reexamination Certificate
2001-11-23
2004-01-13
Johnson, Timothy M. (Department: 2625)
Image analysis
Applications
Biomedical applications
C382S128000, C382S257000, C382S300000, C382S130000, C378S062000
Reexamination Certificate
active
06678399
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention also generally relates to computerized techniques for automated analysis of digital images, for example, as disclosed in one or more of U.S. Pat. Nos. 4,839,807; 4,841,555; 4,851,984; 4,875,165; 4,907,156; 4,918,534; 5,072,384; 5,133,020; 5,150,292; 5,224,177; 5,289,374; 5,319,549; 5,343,390; 5,359,513; 5,452,367; 5,463,548; 5,491,627; 5,537,485; 5,598,481; 5,622,171; 5,638,458; 5,657,362; 5,666,434; 5,673,332; 5,668,888; 5,732,697; 5,740,268; 5,790,690; 5,832,103; 5,873,824; 5,881,124; 5,931,780; 5,974,165; 5,982,915; 5,984,870; 5,987,345; 6,011,862; 6,058,322; 6,067,373; 6,075,878; 6,078,680; 6,088,473; 6,112,112; 6,138,045; 6,141,437; 6,185,320; 6,205,348; 6,240,201; 6,282,305; 6,282,307; 6,317,617 as well as U.S. Pat. No. 6,466,689; 08/398,307 (PCT Publication WO 96/27846); U.S. Pat. No. 5,719,898; Ser. No. 08/900,189; U.S. Pat. Nos. 6,363,163; 6,442,287; 6,335,980; 6,594,378; 6,470,092; Ser. Nos. 09/759,333; 09/760,854; 09/773,636; 09/816,217; 09/830,562; 09/818,831; U.S. Pat. No. 6,483,936; Ser. Nos. 09/860,574; 60/160,790; 60/176,304; and 60/329,322; co-pending application Ser. Nos. 09/990,310, 09/990,377, 10/126,523 PROV, and 10/301,836 PROV; and PCT patent applications PCT/US98/15165; PCT/US98/24933; PCT/US99/03287; PCT/US00/41299; PCT/US01/00680; PCT/US01/01478 and PCT/US01/01479, all of which are incorporated herein by reference.
The present invention includes use of various technologies referenced and described in the above-noted U.S. Patents and Applications, as well as described in the references identified in the following LIST OF REFERENCES by the author(s) and year of publication and cross-referenced throughout the specification by reference to the respective number, in parentheses, of the reference:
LIST OF REFERENCES
1. R. T. Greenlee, M. B. Hill-Harmon, T. Murray, and M. Thun, “Cancer statistics, 200,” Ca-Cancer J Clin 51, 15-36 (2001).
2. C I. Henschke, D. I. McCauley, D. F. Yankelevitz, D. P. Naidich, G. Guinness, O. S. Miettinen, D. M. Libby, M. W. Pasmantier, J. Koizumi, N. K. Altorki, and J. P. Smith, “Early lung cancer action project: overall design and findings from baseline screening,” The Lancet, 354, Jul. 10, 99-105, (1999).
3. D. P. Naidich, C. H. Marshall, C. Gribbin, R. Arams and D. I. McCauley, “Low-dose CT of the lungs: preliminary observations,” Radiology 175, 729-731, (1990).
4. D. F. Yankelevitz, R. Gupta, B. Zhao and C. I. Henschke, “Small pulmonary nodules: Evaluation with repeat CT-preliminary experience,” Radiology 217, 251-256 (2000).
5. C. I. Henschke, “Early lung cancer action project: overall design and findings from baseline screening,” Cancer 89, 2474-2482 (2000).
6. C. I. Henschke, D. P. Naidich, D. F. Yankelevitz, G. McGuinness, D. I. McCauley, J. P. Smith, D. Libby, M. Pasmantier, M. Vazquez, J. Koizumi, D. Flieder, N. K. Altorki and O. S. Miettinen, “Early lung cancer action project: initial findings on repeat screenings,” Cancer 92, 153-159 (2001).
7. C. I. Henschke, D. I. McCauley, D. F. Yankelevitz, D. P. Naidich, G. McGuinness, O. S. Miettinen, D. Libby, M. Pasmantier, J. Koizumi, N. K. Altorki and J. P. Smith, “Early lung cancer action project: a summary of the findings on baseline screening,” Oncologist 6, 147-152 (2001).
8. D. Marshall, K. N. Simpson, C. C. Earle and C. W. Chu, “Economic decision analysis model of screening for lung cancer,” Eur J Cancer 37, 1759-1767 (2001).
9. S. G. Armato, M. L. Giger, C. J. Moran, J. T. Blackburn, K. Doi and H. MacMahon, “A computerized detection of pulmonary nodules on CT scans,” Radiographics 19, 1303-1311 (1999).
10. T. K. Narayan and G. T. Herman, “The use of contrast for automated pulmonary nodule detection in low-dose computed tomography,” Med Phys 26, 427-437 (1999).
11. S. G. Armato, M. L. Giger and H. MacMahon, “Automated detection of lung nodules in CT scans: preliminary results,” Med Phys 28, 1552-61 (2001).
12. S. Yamamoto, I. Tanaka, M. Senda, Y. Tateno, T. linuma and T. Matsumoto, “Image processing for computer-aided diagnosis of lung cancer by CT (LSCT),” Systems and Computers in Japan 25, 67-80 (1994).
13. S. Yamamoto, M. Matsumoto, Y. Tateno et al., “Quoit filter: A new filter based on mathematical morphology to extract the isolated shadow, and its application to automatic detection of lung cancer in X-ray CT,” Proc ICPR II 3-7 (1996).
14. T. Tozaki, Y. Kawata, N. Niki, et al., “Pulmonary organs analysis for differential diagnosis based on thoracic thin-section CT images,” IEEE Trans Nuclear Science 45, 3075-3082 (1998).
15. Y. Kawata, N. Niki, H. Ohmatsu et al., “Quantitative surface characterization of pulmonary nodules based on thin-section CT images,” IEEE Trans Nuclear Science 45, 2132-2138 (1998).
16. A. Kano, K. Doi, H. MacMahon, D. D. Hassell, and M. L. Giger, “Digital image subtraction of temporally sequential chest images for detection of interval change,” Med. Phys. 21, 453-461, (1994).
17. T. Ishida, S. Katsuragawa, K. Nakamura, H. MacMahon and K. Doi, “Iterative image-warping technique for temporal subtraction of sequential chest radiographs to detect interval change,” Medical Physics, 26, 1320-1329 (1999).
18. T. Ishida, K. Ashizawa, R. Engelmann, S. Katsuragawa, H. MacMahon and K. Doi, “Application of temporal subtraction for detection of interval changes in chest radiographs: Improvement of subtraction image using automated initial image matching,” Journal of Digital Imaging, 12, 77-86, (1999).
19. S. Sone, S. Takashima, F. Li, et al., “Mass screening for lung cancer with mobile spiral computed tomography scanner,” Lancet, 351, 1242-1245, (1998).
20. Sone S, Li F, Yang Z-G, et al., “Results of three-year mass screening programmed for lung cancer using mobile low-dose spiral computed tomography scanner,” Br. J. Cancer, 84:25-32 (2001).
21. S. Katsuragawa, K. Doi, H. MacMahon, L. Monnier-Cholley, J. Morishita, and T. Ishida, “Quantitative analysis of geometric-pattern features of interstitial infiltrates indigital chest radiographs: preliminary results,” Journal of Digital Imaging, 9, 137-144 (1996).
22. T. Ishida, S. Katsuragawa, T. Kabayashi, H. MacMahon and K. Doi, “Computerized analysis of interstitial disease in chest radiographs: Improvement of geometric pattern feature analysis,” Medical Physics 24, 915-924 (1997).
23. H. Yoshimura, M. L. Giger, K. Doi, H. MacMahon and S. M. Montner, “Computerized scheme for the detection of pulmonary nodules: Nonlinear filtering technique,” Invest Radiol 27, 124-129 (1992).
The entire contents of each related patent and application listed above and each reference listed in the LIST OF REFERENCES, are incorporated herein by reference.
2. Discussion of the Background
Recently, medical professionals have been able to diagnose lung cancer with the aid of computed tomography (CT) imaging systems. A CT system is a X-ray device used to produce cross sectional images of organs. For instance, a CT system may be used to produce a series of cross sectional images of the human lung. Radiologists are able to examine these series of cross sectional images to diagnose pulmonary nodules.
Lung cancer is the leading cause of cancer mortality for American men and women. Currently the five-year survival rate for patients with lung cancer is less than 15%, whereas this rate for patients with localized and small cancer is improved at 48%. Accordingly, the detection of localized and small lung nodules is an important task for radiologists. Currently, however, only 15% of lung cancer patients are diagnosed at an early stage. For increasing the detection rate of early lung cancer, low-dose helical computed tomography (CT) has been employed in screening programs. Low-dose CT (LDCT) has been shown to be more sensitive than conventional chest radiographs in the detection of small lung nodules. It is therefore desirable for LDCTs to be used during initial examinations for the early detection of lung cancer in screening programs.
However, it is still difficult to detect very subtle nodules. Further, the interpretation of a large number of CT images is time-consumi
Doi Kunio
Ishida Takayuki
Katsuragawa Shigehiko
Choobin Barry
Johnson Timothy M.
University of Chicago
LandOfFree
Subtraction technique for computerized detection of small... does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Subtraction technique for computerized detection of small..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Subtraction technique for computerized detection of small... will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-3186612