• DocumentCode
    2086825
  • Title

    Magnetic Resonance Images Edge Detection Based on Multi-scale Morphology

  • Author

    Wang, Kun ; Wu, Jianhua ; Gao, Liqun ; Pian, Zhaoyu ; Guo, Li

  • Author_Institution
    Northeastern Univ., Shenyang
  • fYear
    2007
  • fDate
    23-27 May 2007
  • Firstpage
    744
  • Lastpage
    747
  • Abstract
    Medical image edge detection is the first and the most important steps in the process of extracting geometric features of the objects in medical image process. Conventionally, edge is detected according to some early brought forward algorithms such as gradient-based algorithm, but they are not so good for noise medical image edge detection. A method is proposed to detect the image edge with Gaussian and salt & pepper noise based on multi-scale morphology in this paper. The resource image is classified into two opposite classes by threshold: object and background. The part which is larger than the threshold uses large-scale structure element to detect the edge, and uses small-scale structure element for the part less than the threshold. Large-scale structure element gets through small-scale structure element´s dilation. The experimental results show that the proposed algorithm is more efficient than the usually used gradient-based edge detecting algorithms.
  • Keywords
    biomedical MRI; edge detection; feature extraction; gradient methods; image classification; image denoising; medical image processing; Gaussian noise; edge detection; geometric features extraction; gradient-based algorithm; image classification; magnetic resonance images; medical image; multiscale image morphology; salt & pepper noise; Biomedical imaging; Educational institutions; Filters; Image edge detection; Information science; Large-scale systems; Magnetic resonance; Morphology; Multi-stage noise shaping; Noise shaping;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Complex Medical Engineering, 2007. CME 2007. IEEE/ICME International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4244-1077-4
  • Electronic_ISBN
    978-1-4244-1078-1
  • Type

    conf

  • DOI
    10.1109/ICCME.2007.4381837
  • Filename
    4381837