Design of compactly supported wavelet to match singularities in medical images

Carrson Fung*, Pengcheng Shi

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations


Analysis and understanding of medical images has important clinical values for patient diagnosis and treatment, as well as technical implications for computer vision and pattern recognition. One of the most fundamental issues is the detection of object boundaries or singularities, which is often the basis for further processes such as organ/tissue recognition, image registration, motion analysis, measurement of anatomical and physiological parameters, etc. The focus of this work involved taking a correlation based approach toward edge detection, by exploiting some of desirable properties of wavelet analysis. This leads to the possibility of constructing a bank of detectors, consisting of multiple wavelet basis functions of different scales which are optimal for specific types of edges, in order to optimally detect all the edges in an image. Our work involved developing a set of wavelet functions which matches the shape of the ramp and pulse edges. The matching algorithm used focuses on matching the edges in the frequency domain. It was proven that this technique could create matching wavelets applicable at all scales. Results have shown that matching wavelets can be obtained for the pulse edge while the ramp edge requires another matching algorithm.

Original languageEnglish
Pages (from-to)358-369
Number of pages12
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - 1 Dec 2002
EventApplications of Digital Image Processing XXV - Seattle, WA, United States
Duration: 8 Jul 200210 Jul 2002


  • Edge detection
  • Filter bank
  • Medical image
  • Signal matching
  • Wavelet


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