• DocumentCode
    3413572
  • Title

    A novel mammographic mass detection approach to combining suprevised and unsuprevised detection algorithms

  • Author

    Dae Hoe Kim ; Jae Young Choi ; Yong Man Ro

  • Author_Institution
    Image & Video Syst. Lab., Korea Adv. Inst. of Sci. & Technol. (KAIST), Daejeon, South Korea
  • fYear
    2012
  • fDate
    Sept. 30 2012-Oct. 3 2012
  • Firstpage
    2857
  • Lastpage
    2860
  • Abstract
    In this paper, we propose the combination of different mass detection algorithms to increase overall mass detection sensitivity for various types of breast masses on mammograms. In particular, supervised and unsupervised mass detection algorithms are effectively combined to maximize complementary effects of both approaches. By combining the aforementioned mass detection algorithms, we can arrive at a combined mass detection approach that makes stronger and accurate detection results. Comparative experiments have been conducted on public mammogram data set. Our results show that the proposed detection system can considerably improve the mass detection sensitivity with relatively small number of false positives, compared to the implementation of using only a single detection solution.
  • Keywords
    cancer; mammography; medical image processing; object detection; mammographic mass detection; mass detection sensitivity; unsuprevised detection algorithms; Breast; Cancer; Databases; Delta-sigma modulation; Detection algorithms; Sensitivity; Shape; Mammography; breast masses; combination; multiple detection; supervised and unsupervised mass detection;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Image Processing (ICIP), 2012 19th IEEE International Conference on
  • Conference_Location
    Orlando, FL
  • ISSN
    1522-4880
  • Print_ISBN
    978-1-4673-2534-9
  • Electronic_ISBN
    1522-4880
  • Type

    conf

  • DOI
    10.1109/ICIP.2012.6467495
  • Filename
    6467495