Skip to main content

Development of a New Algorithm for Detection of Mammographic Masses

  • Chapter
Digital Mammography

Abstract

The rate of breast cancer occurrence is increasing and it is estimated that breast cancer will be the top cause of Japanese women’s cancer mortality quite soon. The examination by mammography is now becoming a major diagnostic tool for finding the cancers at an early stage. However, the number of the expert doctors is not enough in the visual interpretation of mammography and a computer-aided diagnosis (CAD) system to aid physicians is deeply required. Therefore, many research groups have developed automated schemes for detecting masses [1]–[6] and clustered microcalcifications on mammograms.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Lai SM, Li X, Bischof WF (1989) On techniques for detecting circumscribed masses in mammograms. IEEE Trans. Med. Imaging 8, pp 377–386.

    Article  PubMed  CAS  Google Scholar 

  2. Yin FF, Giger ML, Doi K, et al. (1991) Computerized detection of masses in digital mammograms: Analysis of bilateral subtraction images. Med. Phys. 18, pp 955–963.

    Article  PubMed  CAS  Google Scholar 

  3. Yin FF, Giger ML, Vyborny CJ, et al.(1993) Comparison of bilateral-subtraction and single-image processing techniques in the computerized detection of mammographic masses. Invest. Radiol. 28, pp 473–481.

    Article  PubMed  CAS  Google Scholar 

  4. Yin FF, Giger ML, Doi K, et al. (1994) Computerized detection of masses in digital mammograms: Investigation of feature-analysis techniques. J. Digital Imaging 7, pp 18–26.

    Article  CAS  Google Scholar 

  5. Yin FF, Giger ML, Doi K, et al.(1994) Computerized detection of masses in digital mammograms: Automated alignment of breast images and its effect on bilateral-subtraction technique. Med. Phys. 21, pp 445–452.

    Article  PubMed  CAS  Google Scholar 

  6. Zheng B, Chang YH, Gur D (1995) Computerized detection of masses in digitized mammograms using single-image segmentation and a multilayer topographic feature analysis. Acad. Radiol. 2, pp 959–966.

    Article  PubMed  CAS  Google Scholar 

  7. Hara T, Fujita H (1995) Template matching of gray-scale images using a genetic algorithm. The Trans. of IEICE D-II J78-D-II, pp 385–388.

    Google Scholar 

  8. Fujita H, Endo T, Matsubara T, et al. (1995) Automated detection of masses and clustered microcalcifications on mammograms. Proc. SPIE 2434, pp 265–269.

    Google Scholar 

  9. Matsubara T, Fujita H, Endo T, et al. (1996) Development of mass detection algorithm based on adaptive thresholding technique in digital mammograms. In: K Doi et al (eds.), Digital Mammography, Elsevier, Amsterdam, pp 391–396.

    Google Scholar 

  10. Matsubara T, Fujita H, Endo T, et al. (1997) Development of a high-speed processing algorithm for mass detection based on thresholding technique in mammograms. Med. Imaging Tech. 15, pp 1–13.

    Google Scholar 

  11. Kato M, Fujita H, Hara T, et al. (1997) Improvement of automated breast-region-extraction algorithm in a mammogram CAD system. Med. Imaging and Inform. Sci. 14, pp 104–113.

    Google Scholar 

  12. Matsubara T, Kasai S, Seki K, et al. (1998) Development of a computer-aided diagnostic system for mammogram: Improvement of the method of extracting low-density regions during automated mass detection. J. Jpn. Assc. Breast Can. Scr. 7, pp 87–101.

    Article  Google Scholar 

  13. Ostuka O, Kasai S, Hara T, et al. Elimination of false-positive mass candidates in a mammogram CAD system, submitted.

    Google Scholar 

  14. Kasai S, Fujita H, Hara T, et al. Elimination of linear-shape false-positive candidates in an automated detection algorithm for mammographie masses, submitted.

    Google Scholar 

  15. Kasai S, Fujita H, Hara T, et al. Elimination of false-positive candidates by comparing right and left mammograms in an automated mass detection algorithm, submitted.

    Google Scholar 

  16. Kasai S, Fujita H, Hara T, et al. Detection algorithm for masses around thick-mammary-gland regions on mammograms, submitted.

    Google Scholar 

  17. Ueda H, Fujita H, Endo T, et al. (1995) Automated detection of spicules in mass lesions on mammograms. Med. Imaging and Inform. Sci. 12, pp 68–73.

    Google Scholar 

  18. Goto M, Endo T, H Fujita (1996) Studies on diagnostic logic for classifying benign and malignant masses on mammograms. Jpn. J. of Med. Elect, and Biol. Eng. 34, pp 62–67.

    Google Scholar 

  19. Tani Y, Hara T, Fujita H, et al. Development of mass classification system on mammograms, submitted.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1998 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Matsubara, T. et al. (1998). Development of a New Algorithm for Detection of Mammographic Masses. In: Karssemeijer, N., Thijssen, M., Hendriks, J., van Erning, L. (eds) Digital Mammography. Computational Imaging and Vision, vol 13. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-5318-8_22

Download citation

  • DOI: https://doi.org/10.1007/978-94-011-5318-8_22

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-010-6234-3

  • Online ISBN: 978-94-011-5318-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics