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Journal of Digital Imaging

, Volume 14, Supplement 1, pp 217–219 | Cite as

Comparisons of different contrast resolution effects on a computer-aided detection system intended to cluster microcalcifications detected in dense breast images

  • Fátima L. S. Nunes
  • Hamero Schiabel
  • Mauricio C. Escarpinati
  • Cláudio E. Góes
Scientific Posters and Demonstrations

Abstract

Clustered microcalcifications, which are frequently an important signal of possible cancer, are usually hidden in dense breast images, adding more difficulty in mammogram medical analysis. In this work we evaluate the performance of a previously developed computer-aided detection scheme, modified for application to dense breast images. The main focus of this investigation was on the effect of different contrast resolutions on the processing performance. We have processed dense breast images digitized with a and 12 bits to evaluate the performance of this computer-aided detection scheme with different contrast resolutions. As expected, for most of the 12·bit images, the number of detected signals was greater or at least equal to that of the 8·bit images.

Keywords

Dense Breast Contrast Resolution Cluster Detection Digital Mammogram Microcalcification Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Egan RL, McSweeney MB, Sewell CW, et al: Intramammary calcifications without an associated mass in benign and malignant diseases. Radiology 137:1–7, 1980PubMedGoogle Scholar
  2. 2.
    Nishikawa RM, Giger ML, Doi K, et al: Computer-aided detection and diagnosis of masses. and clustered microcalcifications from digital mammograms, in Bowyer KW, Astley S (eds): State of the Art in Digital Mammographic Image Analysis. Singapore, World Scientific Publishing, 1994, pp 82–102CrossRefGoogle Scholar
  3. 3.
    Chan H-P, Doi K, Galtrotra S, et al: Image feature analysis and computer-aided diagnosis in digital radiography. I. Automated detection of microcalcifications in mammography. Med Phys 14:538–548, 1987CrossRefPubMedGoogle Scholar
  4. 4.
    Shen L, Rangayyan RM, Desautels JEL, et al Detection and classification of mammographic calcifications. Int J Pattern Recognition Artif Intelligence 7:1403–1416, 1993CrossRefGoogle Scholar
  5. 5.
    Byrne C, Schairer C, Wolfe J, et al: Mammographic features and breast cancer risk: Effects with time, age, and menopause status. J Natl Cancer Inst 87:1622–1629, 1995CrossRefPubMedGoogle Scholar
  6. 6.
    Nunes FLS, Schiabel H, Frere AF: Digital marnmogra phy: Problems associated to the detection of clustered microcalcification in image processing. IASTED-International Conference on Computer Graphics and Imaging, Halifax, Canada, June 1–3, 1998Google Scholar
  7. 7.
    Nunes FLS, Schiabel H, Patrocinio AC, et al: Breast clustered microcalcifications detection: Influence of the gray scale levels on the performance of a CAD scheme. Proceedings of the International Seminar on Bioelectronic Interfaces III Workshop on Cybernetic Vision, Capinas-Sl’, Brasil, February 23–26, 1999, pp 92–97 .Google Scholar
  8. 8.
    Sickles E: Breast calcifications: Mammographic evaluation. Radiology 160:289–293, 1986PubMedGoogle Scholar

Copyright information

© Society for Imaging Informatics in Medicine 2001

Authors and Affiliations

  • Fátima L. S. Nunes
    • 1
    • 2
  • Hamero Schiabel
    • 1
    • 2
  • Mauricio C. Escarpinati
    • 1
    • 2
  • Cláudio E. Góes
    • 1
    • 2
  1. 1.Departamento de Física e InformáticaUniversidade de Sāo PauloBrasil
  2. 2.Departamento de Engenharia ElétricaUniversidade de Sāo PauloSāo Carlos. SPBrasil

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