Separation of malignant and benign masses using maximum-likelihood modeling and neural networks

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 4684 II, Issue , 2002, Pages 733-741

  Kinnard, Lisa ^a,b   Lo, Shih Chung B ^a   Wang, Paul ^c   Freedman, Matthew ^a   Chouikha, Mohamed ^b  

^a GEORGETOWN UNIVERSITY   (United States)

^b Howard University   (United States)

^c HOWARD UNIVERSITY   (United States)

Author keywords

Breast cancer; Computer assisted diagnosis; Convolution neural networks; Feature extraction

Indexed keywords

BACKPROPAGATION; COMPUTER AIDED DIAGNOSIS; FEATURE EXTRACTION; IMAGE ANALYSIS; MAXIMUM LIKELIHOOD ESTIMATION; NEURAL NETWORKS; TUMORS;

BREAST CANCER;

MAMMOGRAPHY;

EID: 0036032287     PISSN: 0277786X     EISSN: None     Source Type: Journal    
DOI: 10.1117/12.467216     Document Type: Article

References (16)

1. Frankel S.D., Sickel E.A., Curpen B.N., Sollito R.A., Ominsky S.H., Galvin H.B., Initial versus subsequent screening mammography: Comparison of findings and their prognostics significance. AJR, 1995, vol. 164, pp. 1107-1109.
2. Gonzalez R.C., Woods R.E. Digital Image Processing Reading, MA: Addison Wesley, 1992.
3. Heath M, Bower K.W., Kopans D. et al, Current status of the Digital Database for Screening Mammography, Digital Mammography, Kluwer Academic Publishers, 1998, pp. 457-460.
4. Kopans D.B. The positive predictive value of mammography, AJR, 1991, vol. 158, pp. 521-526.
5. Kupinski M.A., Giger M.L., Automated Seeded Lesion Segmentation on Digital Mammograms, IEEE Transactions on Medical Imaging, 1998, vol. 17, no. 4, pp. 510-517.
6. Li L., Zheng Y., Zhang L., Clark R., False-positive reduction in CAD mass detection using a competitive classification strategy, Medical Physics, 2001, Vol. 28, no. 2, pp. 250-258.
7. Li H., Wang Y., Liu K.J.R., Lo S-C., Freedman M.T., Computerized Radiographic Mass Detection - Part I: Lesion Site Selection by Morphological Enhancement and Contextual Segmentation, IEEE Transactions on Medical Imaging, 2001, vol. 20, no. 4, pp. 289-301.
8. Lo S.C., Li H., Wang J., Kinnard L., Freedman M.T., A Multiple Circular Path Convolution Neural Network System for Detection of Mammographic Masses, IEEE Transactions on Medical Imaging, 2002, vol. 21, No. 2, (Accepted for publication).
9. Mendez A.J., Tahoces P.G., Lado M.J., Souto M., Vidal J.J., Computer-aided diagnosis: Automatic detection of malignant masses in digitzed mammograms, Medical Physics, 1998, vol. 25, no. 6, pp. 957-964.
10. Metz C., LABROC Program, ftp://radiology.uchicago.edu/roc.
11. Nystrom L., Rutqvist L.E., Wall S., Lindgren A., Lindqvist M., Ryden S., et. al., Breast cancer screening with mammography: Overview of Swedish randomized trials, Lancet, 1993, vol. 341, pp. 973-978.
12. Petrick N., Chan H-P., Sahiner B., Wei D., An Adaptive Density-Weighted Contrast Enhancement Filter for Mammographic Breast Mass Detection, IEEE Transactions on Medical Imaging, 1996, vol. 15, no. 1, pp. 59-67.
13. Pohlman S., Powell K.A., Obuchowski N.A., Chilcote W.A., Grundfest-Broniatowski S., Quantitative classification of breast tumors in digitized mammograms, Medical Physics, 1996, vol. 23, no. 8, pp. 1336-1345.
14. Sahiner B., Chan H.P., Wei D., Petrick N., Helvie M.A., Adler D.D., Goodsit M.M., Image feature selection by a genetic algorithm: Application to classification of mass and normal breast tissue, Medical Physics, 1996, vol. 23, no. 10, pp. 1671-1684.
15. Shapiro S., Screening: Assessment of current studies, Cancer, 1994, vol. 74, pp.231-238.
16. Tabar L., Fagerberg G., Duffy S., Day N.E., Gad A., Grontoft O. Update of the Swedish two-country program of mammographic screening for breast cancer, Radiology Clinics of North America: Breast Imaging Current Status and Future Directions, 1992, vol. 30, pp. 187-210.