• DocumentCode
    2641458
  • Title

    Digital Mammogram Tumor Preprocessing Segmentation Feature Extraction and Classification

  • Author

    Raman, Valliappan ; Then, Patrick ; Sumari, Putra

  • Author_Institution
    Sch. of Comput. & Design, Swinburne Univ. of Technol., Kuching, Malaysia
  • Volume
    2
  • fYear
    2009
  • fDate
    March 31 2009-April 2 2009
  • Firstpage
    507
  • Lastpage
    511
  • Abstract
    Mammography has been one of the most reliable methods for early detection of breast carcinomas. The main objective of this paper is to detect and segment the tumor from mammogram images that helps to provide support for the clinical decision to perform biopsy of the breast. In this paper, there are two aspects to segmentation in mammography. First is to separate out the mammogram from the background and the identification of putative masses and the pectoral muscle. The extraction approach is done using basic region growing method to identify the tumor. Second is to extract the features from segmented masses and classifies the masses by case base reasoning method. The experimental results are shown in this paper till the first phase of mass segmentation.
  • Keywords
    cancer; case-based reasoning; feature extraction; image segmentation; mammography; medical image processing; tumours; breast biopsy; breast carcinoma; case base reasoning method; clinical decision; digital mammogram; mammogram image; pectoral muscle identification; putative mass identification; region growing method; tumor preprocessing segmentation feature extraction; Breast cancer; Breast neoplasms; Cancer detection; Computer science; Feature extraction; Image processing; Image segmentation; Lesions; Mammography; Shape; case base reasoning; mammogram; segmentation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Science and Information Engineering, 2009 WRI World Congress on
  • Conference_Location
    Los Angeles, CA
  • Print_ISBN
    978-0-7695-3507-4
  • Type

    conf

  • DOI
    10.1109/CSIE.2009.872
  • Filename
    5171391