• DocumentCode
    3111391
  • Title

    Breast Tumor Segmentation Based on Level-Set Method in 3D Sonography

  • Author

    Yu-Chih Lin ; Yu-Len Huang ; Dar-Ren Chen

  • Author_Institution
    Dept. of Comput. Sci., Tunghai Univ., Taichung, Taiwan
  • fYear
    2013
  • fDate
    3-5 July 2013
  • Firstpage
    637
  • Lastpage
    640
  • Abstract
    Malignant and benign breast tumors exist discrepancies in their shape and size on sonography. Morphological information provided by the contour of tumor is important in clinical diagnosis. However, ultrasound images contain noises and tissue texture, clinical diagnosis must highly depend on expertise experience. The manual way to sketch 3D breast tumor contour is a time-consuming and complicated task. Automatic contour which provides similar contour with manual sketch of the breast tumor in the ultrasonic images might assist physicians in making an accurate diagnosis. This study presents an efficient segmentation procedure which based on level-set method for automatically detecting contours of breast tumors in 3D sonography. The proposed method always identified similar contours as were obtained by manual contouring of the breast tumor and could save much of the time required to sketch precise contours.
  • Keywords
    biomedical ultrasonics; image denoising; image texture; medical image processing; tumours; 3D sonography; benign breast tumors; breast tumor segmentation; clinical diagnosis; level-set method; malignant breast tumors; morphological information; noises; tissue texture; ultrasound images; Breast cancer; Breast tumors; Image segmentation; Silicon; Three-dimensional displays; 3D sonography; breast cancer; image segmentation; level-set method; region growing method;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), 2013 Seventh International Conference on
  • Conference_Location
    Taichung
  • Type

    conf

  • DOI
    10.1109/IMIS.2013.114
  • Filename
    6603748