• DocumentCode
    704698
  • Title

    An improved edge detection using morphological Laplacian of Gaussian operator

  • Author

    Anand, Ashish ; Tripathy, Sanjaya Shankar ; Kumar, R. Sukesh

  • Author_Institution
    Dept. of ECE, Birla Inst. of Technol., Ranchi, India
  • fYear
    2015
  • fDate
    19-20 Feb. 2015
  • Firstpage
    532
  • Lastpage
    536
  • Abstract
    Generally medical images are of low contrast. They need to be enhanced for computer diagnosis as well as for observation and analysis by a doctor. Edge detection is one of the basic yet important processes of image segmentation that leads to detection of different organs in a medical image. Presently a number of edge detection algorithms are available, but they do not always give satisfactory results. In this paper, a new algorithm is proposed which is based on the study of mathematical morphology and Laplacian of Gaussian. The proposed algorithm combines the advantages of both techniques for better detection of edge and good image contrast. We also present a study of different gradient based operators and mathematical morphology used for edge detection. The results of the proposed algorithm are compared with those of other methods showing the improvement in the result of proposed algorithm.
  • Keywords
    Gaussian processes; biological organs; edge detection; gradient methods; image segmentation; mathematical morphology; medical image processing; Gaussian operator morphological Laplacian; computer diagnosis; edge detection; gradient based operator; mathematical morphology; medical image segmentation; organ detection; Biomedical imaging; Bones; Detectors; Image edge detection; Laplace equations; Morphology; Signal processing; Edge detection; gradient based operators; image segmentation; laplacian of Gaussian; mathematical morphology;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signal Processing and Integrated Networks (SPIN), 2015 2nd International Conference on
  • Conference_Location
    Noida
  • Print_ISBN
    978-1-4799-5990-7
  • Type

    conf

  • DOI
    10.1109/SPIN.2015.7095391
  • Filename
    7095391