• DocumentCode
    2571362
  • Title

    Fully automatic breast segmentation in 3D breast MRI

  • Author

    Wang, Lei ; Platel, Bram ; Ivanovskaya, Tatyana ; Harz, Markus ; Hahn, Horst K.

  • Author_Institution
    Inst. for Med. Image Comput., Fraunhofer MEVIS, Bremen, Germany
  • fYear
    2012
  • fDate
    2-5 May 2012
  • Firstpage
    1024
  • Lastpage
    1027
  • Abstract
    In computer-aided diagnosis of breast MRI, a precise segmentation of the breast is often required as a fundamental step to facilitate further diagnostic tasks, e.g., breast density measurement, lesion detection and automatic reporting. In this work, a fully automatic method dedicated to breast segmentation is proposed, which comprises four major steps: sheet-like structures enhancement, pectoralis muscle boundary segmentation, breast-air boundary segmentation and breast extraction. To validate the proposed method, the segmented breast boundaries of 84 breast MR images, acquired in five different sites with variant imaging protocols, were compared to the manual segmentation. An average distance of 2.56mm with a standard deviation of 3.26mm was achieved.
  • Keywords
    biological organs; biomedical MRI; gynaecology; image enhancement; image segmentation; medical image processing; muscle; 3D breast MRI; automatic breast segmentation; breast MR images; breast density measurement; breast extraction; computer-aided diagnosis; lesion detection; pectoralis muscle boundary segmentation; sheet-like structure enhancement; standard deviation; variant imaging protocols; Accuracy; Breast tissue; Image segmentation; Magnetic resonance imaging; Muscles; Standards; Hessian filter; breast MRI; breast segmentation; region growing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Biomedical Imaging (ISBI), 2012 9th IEEE International Symposium on
  • Conference_Location
    Barcelona
  • ISSN
    1945-7928
  • Print_ISBN
    978-1-4577-1857-1
  • Type

    conf

  • DOI
    10.1109/ISBI.2012.6235732
  • Filename
    6235732