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Edge detection of femur bone - A comparative study

  • Harish Kumar*
  • , Ashvini Chaturvedi
  • *Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Edge detection of femur in X - ray images is an important pre processing step in segmentation and 3 - D reconstruction of femur. A typical femur image is generally very noisy. A lot of edges caused by the muscles and other bones can easily mislead the edge detection algorithm. Particularly, the femoral head overlapping the pelvic bone makes it very difficult to get a clear edge of the femur head. The edge caused by the abdominal muscles around the femur shaft can also mislead the edge detection algorithm. These extraneous edges and noise make edge detection very difficult and challenging, which is not well solved. Classical edge detectors fail miserably due to the high inhomogeneous nature of the femur X - ray images. This paper compares a new approach to edge detection of femur X - ray images using Wavelet transforms with classical edge detectors. The Wavelet based edge detection algorithm combines the coefficients of wavelet transforms on a series of scales and significantly improves the result. It is found that Wavelet based technique works much better than classical edge detectors.

    Original languageEnglish
    Title of host publicationProceedings of the 2010 International Conference on Signal and Image Processing, ICSIP 2010
    Pages281-285
    Number of pages5
    DOIs
    Publication statusPublished - 01-12-2010
    Event3rd IEEE International Conference on Signal and Image Processing, ICSIP 2010 - Chennai, India
    Duration: 15-12-201017-12-2010

    Publication series

    NameProceedings of the 2010 International Conference on Signal and Image Processing, ICSIP 2010

    Conference

    Conference3rd IEEE International Conference on Signal and Image Processing, ICSIP 2010
    Country/TerritoryIndia
    CityChennai
    Period15-12-1017-12-10

    All Science Journal Classification (ASJC) codes

    • Computer Vision and Pattern Recognition
    • Signal Processing

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