Computer diagnosis of mammographic masses

Proceedings - Applied Imagery Pattern Recognition Workshop

Volumn 2000-January, Issue , 2000, Pages 166-172

  Velthuizen, Robert P ^a,b  

^a UNIVERSITY OF SOUTH FLORIDA   (United States)

^b UNILEVER RESEARCH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEX NETWORKS; COMPUTER VISION; FEATURE EXTRACTION; MAMMOGRAPHY; NEAREST NEIGHBOR SEARCH; NETWORK LAYERS;

AREA UNDER THE ROC CURVE; K-NEAREST NEIGHBORS CLASSIFIERS; NEURAL NETWORK CLASSIFIER; RELEVANT FEATURES; SEGMENTATION RESULTS; SEQUENTIAL SELECTION; THREE-LAYER NEURAL NETWORKS; TWO-FOLD-CROSS-VALIDATION;

PATTERN RECOGNITION;

EID: 5644236289     PISSN: 21642516     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/AIPRW.2000.953621     Document Type: Conference Paper

References (38)

1. Wright CJ, Mueller CB Screening mammography and public health policy: the need for perspective. Lancet 346 (8966): 29-32, 1995.
2. Beam CA, Layde PM, Sullivan DC Variability in the interpretation of screening mammograms by US radiologists. Findings from a national sample. Arch Intern Med 156 (2): 209-213, 1996.
3. Dilhuydy MH, Barreau B. The debate over mass mammography: is it beneficial for women? Eur J Radiol 24 (2): 86-93, 1997.
4. Wright CJ. Screening mammography: balancing the harm, the cost and the benefit. In: Ismail Jatoi, Editor: Breast Cancer Screening. New York, Cahpman & Hall: 65-80, 1997.
5. Kopans DB. The case in favor of mammographie screening for women in their forties. In: Ismail Jatoi, Editor: Breast Cancer Screening. New York, Cahpman & Hall: 9-34, 1997.
6. Rosenberg RD, Lando JF, Hunt W.C., Darling RR, Williamson MR, Linver MN, Gilliland F.D., Key CR. The New Mexico Mammography Project. Screening mammography performance in Albuquerque, New Mexico, 1991 to 1993. Cancer 78(8): 1731-1739, 1996.
7. Vizcaino I, Salas D, Vilar J.S., Ruiz-Perales F, Herranz C, Ibanez J. Breast cancer screening: first round in the population-based program in Valencia, Spain. Collaborative Group of Readers of the Breast Cancer Screening Program of the Valencia Community. Radiology 206 (1): 253-260, 1998
8. Sterns EE. The abnormal mammogram in women with clinically normal breasts. Can J Surg 38 (2): 168-172, 1995.
9. Elmore JG, Barton MB, Moceri V.M., Polk S., Arena PJ, Fletcher SW. Ten-year risk of false positive screening mammograms and clinical breast examinations. New England Journal of Medicine 338 (16): 1089-1096, 1998.
10. Liberman L, Feng TL, Dershaw D.D., Morris EA, Abramson AF. US-guided core breast biopsy: use and cost-effectiveness. Radiology 208 (3): 717-723, 1998.
11. Liberman L, Abramson AF, Squires F.B., Glassman JR, Morris EA, Dershaw DD. The breast imaging reporting and data system: positive predictive value of mammographie features and final assessment categories. AJR American Journal of Roentgenology 171 (1): 35-40, 1998.
12. Berube M, Curpen B, Lalonde L., Ouimet-Oliva D. Level of suspicion of a mammographie lesion: use of features defined by BI-RADS lexicon and correlation with large-core breast biopsy. Can Assoc Radiol J, 49(4), 223-228, 1998.
13. Ong G, Austoker J. Recalling women for further investigation of breast screening: women's experiences at the clinic and afterwards. J Public Health Med 19 (1): 29-36, 1997.
14. Scaf-Klomp W., Sanderman R, van de Wiel HB, Otter R, van den Heuvel WJ. Distressed or relieved? Psychological side effects of breast cancer screening in The Netherlands. J Epidemiol Community Health 51 (6): 705-710, 1997.
15. Lindfors KK, O'Connor J, Acredolo CR, Liston SE Short interval follow-up mammography versus immediate core biopsy of benign breast lesions: assessment of patient stress. ARJ American Journal of Roentgenology 171: 55-58, 1998.
16. Adler DD, Wahl RL New methods for breast cancer imaging. In: Harris JR, Lippman ME, Morrow M, Hellman S, editors: Diseases of the breast, pp 84-98. Philadelphia, Lippincott-Raven, 1996.
17. Kopans DB. Imaging analysis of breast lesions. In: Harris JR, Lippman ME, Morrow M, Hellman S, editors: Diseases of the breast, pp 71-84. Philadelphia, Lippincott-Raven, 1996.
18. Ciatto S, Rosselli Del Turco M, Ambrogetti D, Bravetti P, Catarzi S, Morrone D, Cariaggi MP. Solid nonpalpable breast lesions. Success and failure of guided fine-needle aspiration cytology in a consecutive series of 2444 cases. Acta Radiologica 38 (5): 815-820, 1997.
19. Feichter GE, Haberthur F, Gobat S., Dalquen P. Breast cytology. Statistical analysis and cytohistologic correlations. Acta Cytologica 41 (2): 327-332, 1997.
20. Velthuizen RP, Gangadharan D. Mammographic Mass Classification: Initial Results. Presented at the SPIE Medical Imaging Conference, February 14, 2000. To appear in SPIE Proceedings Volume 3979. (Included in the Appendix).
21. Li H-D, Kallergi M., Clarke LP, Jain VK, Clark RA. Markov random field for tumor detection in digital mammography. IEEE Transactions in Medical Imaging 14(3): 565-576, 1995.
22. Li. L, Qian W., Clarke LP and Clark RA. Digital Mammography: CAD method for mass detection using multiorientation and multiresolution wavelet transforms. Academic Radiology 4 (11): 724-731, 1997.
23. Fahlman S. Faster-Learning Variations on Back-Propagation: An Empirical Study. Proceedings of 1988 Connectionist Models Summer School, Morgan Kauffman, 1988.
24. Gong G. Cross-validation, the jackknife, and the bootstrap: excess error estimation in forward logistic regression. Journal of the American Statistical Association, 81(393), 108-113, 1986.
25. Tourassi GD, Floyd CE The effect of data sampling on the performance evaluation of artificial neural networks in medical diagnosis. Medical Decision making 17(2), 186-192, 1997.
26. Baker JA, Kornguth PJ, Lo J.Y., Williford ME, Floyd CE. Breast Cancer: Prediction with artificial neural network based on BI-RADS standardized lexicon. Radiology 196: 817-822, 1995.
27. Kahn CE J.r., Roberts LM, Shaffer K.A., Haddawy. Construction of a Bayesian network for mammographic diagnosis of breast cancer. Comput. Biol. Med. 27 (1): 19-29, 1997.
28. Huo Z and Giger ML. Evaluation of a Computerized Segmentation Method Based on the Performances of an Automated Classification Method. Presented at the SPIE Medical Imaging Conference, February 16, 2000. SPIE Volume 3981.
29. Velthuizen RP, Clarke LP, Phuphanich S, Hall L.O., Bensaid AM, Arrington JA, Greenberg HM and Silbiger ML. Unsupervised measurement of brain tumor volume on MR images. Journal of Magnetic Resonance Imaging 5(5), 594-605, 1995.
30. Huo Z, Giger ML, Vyborny C.J., Wolverton DE, Schmidt RA, Doi K. Automated computerized classification of malignant and benign masses on digitized mammograms. Academic Radiology 5: 155-168, 1998.
31. Huo Z, Giger ML Robustness of a computerized scheme for classification of malignant and benign masses on digitized mammograms. First International Workshop on Computer Aided Diagnosis, Chicago, IL, September 20-23, 1998.
32. Shen D and Ip HHS, 1999. Discriminative wavelet shape descriptors for recognition of 2-D patterns. Pattern Recognition 32(2), 151-165.
33. Kilday J, Palmieri F, Fox M.D., 1993. Classifying mammographie lesions using computerized image analysis. IEEE Trans Med Imag 12(4): 664-669.
34. Bruce LM and Adhami RR, 1999. Classifying Mammographie Mass Shapes Using the Wavelet Transform Modulus-Maxima Method. IEEE Trans Med Imag 18(12), 1170-1177.
35. Polakowski WE, Coumoyer DA, Rogers S.K., DeSimio MP, Ruck DW, Hoffmeister JW, Raines R.A., 1997. Computer-aided breast cancer detection and diagnosis of masses using difference of Gaussians and derivative-based feature saliency. IEEE Trans Med Imag 16: 811-819.
36. Laws K, 1980. Rapid texture identification. Proc. SPIE 238, 376-380.
37. Devijver, P.A. and Kittler, J. Pattern Recognition: A Statistical Approach. London: Prentice Hall International, 1982.
38. Vafaie, H. and De Jong, K. Robust Feature Selection Algorithms. In: Proceedings of the Int. Conf on Tools with AI, Boston, MA, pp 356-364, IEEE Comp. Soc. Press, 1993.