• DocumentCode
    621941
  • Title

    Breast cancer detection: A review on mammograms analysis techniques

  • Author

    Hela, Boulehmi ; Hela, Mahersia ; Kamel, Hany ; Sana, Boussetta ; Najla, Mnif

  • Author_Institution
    Ecole Nat. d´Ing. de Tunis, Univ. Tunis El Manar, Tunis, Tunisia
  • fYear
    2013
  • fDate
    18-21 March 2013
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    Breast cancer is the most common cancer among women over 40 years. Studies have shown that early detection and appropriate treatment of breast cancer significantly increase the chances of survival. They have also shown that early detection of small lesions boosts prognosis and leads to a significant reduction in mortality. Mammography is in this case the best diagnostic technique for screening. However, the interpretation of mammograms is not easy because of small differences in densities of different tissues within the image. This is especially true for dense breasts. This paper is a survey of the automatic early detection of breast cancer by analyzing mammographic images. This analysis could provide radiologists a better understanding of stereotypes and provides, if it is detected at an early stage, a better prognosis inducing a significant decrease in mortality.
  • Keywords
    cancer; diagnostic radiography; mammography; medical image processing; patient treatment; tumours; automatic early-breast cancer detection; breast cancer detection; breast cancer treatment; diagnostic technique; lesion detection; mammographic image analysis; mortality reduction; prognosis improvement; tissue densities; Breast cancer; Databases; Image segmentation; Lesions; Neural networks; breast cancer; breast density; early detection; mammograms analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems, Signals & Devices (SSD), 2013 10th International Multi-Conference on
  • Conference_Location
    Hammamet
  • Print_ISBN
    978-1-4673-6459-1
  • Electronic_ISBN
    978-1-4673-6458-4
  • Type

    conf

  • DOI
    10.1109/SSD.2013.6563999
  • Filename
    6563999