Reduction of breast biopsies with a modified self-organizing map

IEEE Transactions on Neural Networks

Volumn 8, Issue 6, 1997, Pages 1386-1396

  Zheng, Yi ^a,b,c   Greenleaf, James F ^a,b   Gisvold, John J ^b  

^a IEEE   (United States)

^b MAYO CLINIC   (United States)

^c ST CLOUD STATE UNIVERSITY   (United States)

Author keywords

Breast biopsy; Computer vision radiologist assistant; Modified self organized map; Texture analysis; Ultrasound image

Indexed keywords

COMPUTER VISION; DIAGNOSIS; DISEASES; MEDICAL IMAGING; ONCOLOGY; ULTRASONIC IMAGING;

BREAST BIOPSY; COMPUTER VISION RADIOLOGIST ASSISTANT; DIGITAL SONOGRAPHIC BREAST IMAGE;

NEURAL NETWORKS;

EID: 0031274866     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/72.641462     Document Type: Article

References (18)

1. T. Kobayashi, "Gray-scale echography for breast cancer," Radiology, vol. 122, pp. 207-214, 1977.
2. C. Cole-Beuglet, "Ultrasound," Mammography, Thermography, and Ultrasound in Breast Cancer Detection, L. W. Bassett and R. H. Gold, Eds. New York: Grune and Stratton, 1982, pp. 151-167.
3. K. T. W. Taylor, "Can complementary ultrasound reduce the number of biopsies of benign breast masses?" in Proc. Radiologic Soc. North Amer., Abst. 433, Mar. 1993.
4. A. T. Stavros, D. I. Thickman, C. L. Rapp, M. A. Dennis, S. H. Parker, and G. A. Sisney, "Solid breast nodules: Use of sonography to distinguish between benign and malignant lesions," Radiology, vol. 196, no. 1, pp. 123-134, July 1995.
5. S. L. Hagen-Ansert, "Breast," Diagnostic Ultrasonography, S. L. Hagen-Ansert, Ed. Chicago, IL: Year Book, 1995, pp. 333-349.
6. Y. Zheng and J. F. Greenleaf, "The effect of concave and convex weight adjustment on self-organizing maps," IEEE Trans. Neural Networks, vol. 7, pp. 87-96, 1996.
7. V. Goldberg, A. Manduca, D. L. Ewert, J. J. Gisvold, and J. F. Greenleaf, "Improvement in specificity of ultrasonography for diagnosis of breast tumors by means of artificial intelligences," Med. Phys., vol. 19, no. 6, pp. 1475-1481, 1992.
8. B. S. Garra, B. H. Krasner, S. C. Horri, S. Ascher, S. K. Mun, and R. K. Zeman, "Improving the distinction between benign and malignant breast lesions: The values of sonographic texture analysis," Ultrason. Imaging, vol 15, pp. 267-285, 1993.
9. T. Kohonen, "Self-organized formation of topologically correct feature maps," Biol. Cybern., vol. 43, pp. 59-69, 1982.
10. _, "The self-organizing map," Proc. IEEE, vol 78, no. 9, pp. 1464-1480, 1990.
11. M. M. Galloway, "Texture analysis using gray level run lengths," Computer Graphics Image Proc., vol. 4, pp. 172-179, 1975.
12. A. Chu, C. M. Sehgal, and J. F. Greenleaf, "Use of gray value distribution of run lengths for texture analysis," Pattern Recognition Lett., vol. 11, pp. 415-420, 1990.
13. N. J. Pressman, "Markovian analysis of cervical cell images," J. Histochem. Cytochem., vol. 24, pp. 138-144, 1976.
14. T. Taxt, A. Lundervold, B. Fuglaas, H. Lien, and V. Abeler, "Multispectral analysis of uterine corpus tumors in magnetic resonance imaging," Magn. Reson. Medicine, vol. 23, pp. 55-76, 1992.
15. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning internal representations by error propagation," Parallel Distributed Processing, vol. 1. Cambridge, MA: MIT Press, 1986, pp. 318-362.
16. A. H. Kramer and A. Sangiovanni-Vincentelli, "Efficient parallel learning algorithms for neural networks," Advanced Neural Information Processing System, vol. 1. San Mateo, CA: Morgan Kaufman, 1989, pp. 40-48.
17. S. E. Fahlman and C. Lebiere, "The cascade-correlation learning architecture," Advanced Neural Information Processing System, vol. 2. San Mateo, CA: Morgan Kaufman, 1990, pp. 40-48.
18. S. M. Weiss and C. Kulikowski, Computer Systems That Learn. San Mateo, CA: Morgan Kaufmann, 1990, pp. 30-39.