• DocumentCode
    1756018
  • Title

    Computer-Aided Breast Cancer Detection Using Mammograms: A Review

  • Author

    Ganesan, Kavita ; Acharya, U.R. ; Chua, Chua Kuang ; Min, L.C. ; Abraham, K. Thomas ; Ng, Kung Bo

  • Author_Institution
    Dept. of ECE, Ngee Ann Polytech., Singapore, Singapore
  • Volume
    6
  • fYear
    2013
  • fDate
    2013
  • Firstpage
    77
  • Lastpage
    98
  • Abstract
    The American Cancer Society (ACS) recommends women aged 40 and above to have a mammogram every year and calls it a gold standard for breast cancer detection. Early detection of breast cancer can improve survival rates to a great extent. Inter-observer and intra-observer errors occur frequently in analysis of medical images, given the high variability between interpretations of different radiologists. Also, the sensitivity of mammographic screening varies with image quality and expertise of the radiologist. So, there is no golden standard for the screening process. To offset this variability and to standardize the diagnostic procedures, efforts are being made to develop automated techniques for diagnosis and grading of breast cancer images. A few papers have documented the general trend of computer-aided diagnosis of breast cancer, making a broad study of the several techniques involved. But, there is no definitive documentation focusing on the mathematical techniques used in breast cancer detection. This review aims at providing an overview about recent advances and developments in the field of Computer-Aided Diagnosis (CAD) of breast cancer using mammograms, specifically focusing on the mathematical aspects of the same, aiming to act as a mathematical primer for intermediates and experts in the field.
  • Keywords
    CAD; cancer; gynaecology; mammography; medical diagnostic computing; reviews; American Cancer Society; breast cancer image grading; computer-aided breast cancer detection; computer-aided diagnosis; diagnostic procedures; image quality; inter-observer errors; intra-observer errors; mammograms; mammographic screening sensitivity; mathematical techniques; medical image analysis; radiologist expertise; radiologists; review; screening process; Biomedical imaging; Breast cancer; Feature extraction; Mammography; Medical tests; Noise measurement; Wavelet transforms; Breast cancer; classifiers; computer-aided diagnosis (CAD); digital mammography; feature extraction techniques; Breast Neoplasms; Female; Humans; Mammography; Radiographic Image Interpretation, Computer-Assisted;
  • fLanguage
    English
  • Journal_Title
    Biomedical Engineering, IEEE Reviews in
  • Publisher
    ieee
  • ISSN
    1937-3333
  • Type

    jour

  • DOI
    10.1109/RBME.2012.2232289
  • Filename
    6378398