An adaptive density-weighted contrast enhancement filter for mammographie breast mass detection

IEEE Transactions on Medical Imaging

Volumn 15, Issue 1, 1996, Pages 59-67

  Patrick, Nicholas ^a   Chan, Heang Ping ^a   Sahiner, Berkman ^a   Wei, Datong ^b  

^a UNIVERSITY OF MICHIGAN MEDICAL SCHOOL   (United States)

^b UNIVERSITY OF CHICAGO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE FILTERING; ALGORITHMS; COMPUTER AIDED DIAGNOSIS; EDGE DETECTION; FEATURE EXTRACTION; IMAGE SEGMENTATION; NEURAL NETWORKS; ONCOLOGY;

DENSITY WEIGHTED CONTRAST ENHANCEMENT FILTER; LAPLACIAN GAUSSIAN EDGE DETECTION; MAMMOGRAPHIC BREAST MASS DETECTION;

MEDICAL IMAGING;

EID: 0030081841     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/42.481441     Document Type: Article

References (27)

1. C. C. Boring, T. S. Squires, T. Tong, and S. Montgomery, Cancer statistics, 1994, CA-A Cancer J. Clinicians, vol. 44, no. 1, pp. 7-26, Jan. 1994
2. S. Shapiro, W. Vcnct, P. Strax, L. Vend, and R. Roeser, Ten-tofouiteen-year effect of screening on breast cancer mortality, JNCI, vol. 69, pp. 349, 1982
3. R. G. Lester, The contributions of radiology to the diagnosis, management, and eure of breast cancer, Radio., vol. 151, p. 1, 1984
4. M. Moskowitz, Benefit and risk, in Breast Cancer Detection: Mammography and Oiher Methods in Breast Imaging, 2nd ed., L. W. Bassett and R. H. Gold, Eds. New York: Grime and Stratum, 1987
5. [51 L. Tabâr and P. B. Dean, The control of breast cancer through mammography screening: What is the evidence, Radial. Clin. N. Amer., vol. 25, no. 5, pp. 993-1005, Sept. 1987
6. J. Caseldine, R. Blarney, E. Roebuck, and C. Elston, Breast Disease for Radiographers. Toronto, Canada: Wright, 1988
7. J. E. Martin, M. Moskowitz, and J. R. Milbtath. Breast cancer missed by mammography, Am. J. Roentgeno., vol. 132, pp. 737-739, May 1979
8. R. E. Bird, T. W. Wallace, and B. C. Yankaskas, Analysis of cancers missed at screening mammography, Radial., vol. 184, pp. 613-617, Sept. 1992
9. M. G. Wallis, M. T. Walsh, and J. R. Lee, A review of false negative mammography in a symptomatic population, Clin. Radiai, vol. 44, pp. 13-15, 1991
10. F. Shtern, C. Stelling, B. Goldberg, and R. Hawk'ms, Novel technologies in breast imaging: National Cancer Institute perspective, in Second Po.st-Gradnate Course Syllabus, Soc. of Breast Imaging, Orlando, FL, May 1995, pp. 153-156
11. H. P. Chan, K. Doi, C. J. Vyborny, R. A. Schmidt, C. E. Metz, K. L. Lam, T. Ogura, Y. Wu, and H. MacMahon, Improvement in radiologists' detection of clustered iiiicrocalcificatioiis on mammograms, Investigative Radiai, vol. 25, no. 10, pp. 1102-1110, Oct. 1990
12. W. P. Kegelmeyer Jr., Computer detection of stellate lesions in mammograms, in Proc. SPIE Biomed. Image Processing, 1992, vol. 1660, pp. 446-154.
13. S. M. Lai, X. Li, and W. F. Bischof, On techniques for detecting circumscribed masses in mammograms, IEEE Trans. Med. Imag., vol. 8, no. 4, pp. 377-386, Dec. 1989.
14. W. Qian, L. P. Clarke, M. Kallergi, and R. A. Clark, Tree-structure nonlinear filters in digital mammography, IEEE Trans. Med. Imag., vol 13, no. 1, pp. 25-36, Mar. 1994.
15. D. Brzakovic, X. M. Luo, and P. Bzrakovic, Ar approach to automate detection of rumors in mammography, IEEE Trans. Med. Imag., vol 9, no. 3, pp. 233-241, Sept. 1990.
16. F. F. Yin, M. L. Giger, K. Doi, C. E. Metz, R. A. Vyborny, and C. J Schmidt, Computerized detection of masses in digital mammograms Analvsis of bilateral subtraction images, Med. Phys., vol. 18, no. 5, pp 955-963, Sept. 1991.
17. T. K. I.au and W. F. Bischof, Automated detection of breast tumor using the asymmetry approach, Compui. Biomed. Res., vol. 24, pp 273-295, 1991.
18. W. P. Kcgelmeycr Jr., J. M. Prurieda, P. D. Bourland, A. Hillis, M. W Riggs, and M. L. Nipper, Computer-aided mammographie screening fo spictilated lesions, Radial, vol. 191, no. 2, pp. 331-337, May 1994.
19. T. Peli and J. S. Lim. Adaptive filtering for image enhancement, Opt Eng., vol. 21, no. 1, pp. 108-112, 1982.
20. W. K. Pratt, Digital Image Processing. New York: Wiley, 1978.
21. D. Marr and E. Hildreth, Theory of edge detection, in froc. Roya Soc. London Ser. B Bio. Sei., vol. 207, 1980 pp. 187-217.
22. W. H. H. J. Lunscher and M. P. Beddoes, Optimal edge detector desig i: Parameter selection and noise effects, IEEE Trans. Pattern Anal Machine Intell., vol. 8, no. 2, pp. 154-176, Mar. 1986.
23. Optimal edge detection design ii: Coefficient quantization, IEEE Traits. Pattern Anal. Machine Intell, vol. 8, no. 2, pp. 178-187 Mar. 1986
24. P. A. Lachenbruch. Discriminant Analysis New York: Hafner, 1975.
25. J. A. Freeman and D. M. Skapura, Neural Networks: Algorithms Applications and Programming Techniques. Reading, MA: Addi son-Wesley, 1991.
26. H. P. Chan, D. Wei, M. A. Helvie, B. Sahiner, D. D. Adler, M. M. Good sitt, and N. Petrick, Computer-aided classification of mammographi masses and normal tissue: Linear discriminant analysis in texture featur space, Phys. Med. Bio., vol. 40, pp. 857-876, 1995.
27. D. Wei, H. P. Chan, M. A. Helvie, B. Sahiner, N. Petrick, D. D. Adler and M. M. Ooodshitt, Classification of mass and normal breast tissu on digital mammograms: Multiresolution texture analysis, Med. Phys. May 1995.