• DocumentCode
    3017165
  • Title

    The Correlation Analysis between Breast Density and Cancer Risk Factor in Breast MRI Images

  • Author

    Ding-Horng Chen ; Yi-Chen Chang ; Pai-Jun Huang ; Chia-Hung Wei

  • Author_Institution
    Dept. of Comput. Sci. & Inf. Eng., Southern Taiwan Univ. of Sci. & Technol., Tainan, Taiwan
  • fYear
    2013
  • fDate
    2-5 July 2013
  • Firstpage
    72
  • Lastpage
    76
  • Abstract
    Breast cancer is one of the most common malignancy in women. Recently, the development in medical imaging technology increases the diagnosis effectiveness in predicting breast tumor in the early stage. The trend in breast cancer diagnose is to predict what kind of breast cancer could be happened instead of detecting the disease. In this paper, a breast magnetic resonance imaging is applied to compute breast density. The breast density value is used to find the correlation with the cancer risk factors such as the age, the cancer type, the tumor location, the tumor size and the cancer tumor grading. The statistics tools one-way single factor ANOVA, F-test and descriptive statistics is used to analyze the correlation. Our study found that breast density with the degree of differentiation of tumor cells in infiltrating ductal carcinoma has a significant relevance (P<;0.05). Because of the result is beyond our expectation, we implied the result may be caused because of the lack of a large enough amount of testing samples. We hope that we can extend the result of this study to find out the correlation pattern to accurate assessment the risk coefficient of breast cancer by calculating breast density, and providing physicians to prognostic assessment.
  • Keywords
    biomedical MRI; cancer; correlation methods; medical image processing; object detection; statistical analysis; tumours; F-test; breast MRI images; breast cancer diagnosis; breast density; breast magnetic resonance imaging; breast tumor prediction; cancer risk factor; cancer tumor grading; correlation analysis; correlation pattern; descriptive statistics; disease detection; infiltrating ductal carcinoma; malignancy; medical imaging technology; one-way single factor ANOVA; prognostic assessment; risk coefficient assessment; statistics tools; tumor cells differentiation; tumor location; tumor size; Biomedical imaging; Breast cancer; Correlation; Magnetic resonance imaging; Tumors; Breast Cancer; Breast Density; Breast MRI; Statistical Tools;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Biometrics and Security Technologies (ISBAST), 2013 International Symposium on
  • Conference_Location
    Chengdu
  • Print_ISBN
    978-0-7695-5010-7
  • Type

    conf

  • DOI
    10.1109/ISBAST.2013.14
  • Filename
    6597669