Applications of Wavelet Transform in Registration, Segmentation, Denoising, and Compression of Medical Images

Conference paper
Part of the Lecture Notes in Bioengineering book series (LNBE)


Wavelet transforms and other multiscale analysis functions have been used for compact signal and image representations in denoising, compression, and feature detection processing problems. The wavelet transform itself offers great design flexibility. Basis selection, spatial-frequency tiling, and various wavelet threshold strategies can be optimized for best adaptation to a processing application, data characteristics, and feature of interest. Fast implementation of wavelet transforms using a filter-bank framework enables real-time processing capability. Instead of trying to replace standard image processing techniques, wavelet transforms offer an efficient representation of the signal, finely tuned to its intrinsic properties. By combining such representations with simple processing techniques in the transform domain, multiscale analysis can accomplish remarkable performance and efficiency for many image processing problems.


Wavelet transform using Matlab Image edge detection Segmentation Registration De-noising Lossless image compression Digital imaging and communications in medicine (DICOM) Security issue in transmission Transmission of medical images Measuring lossless compression effectiveness parameters Compression algorithm 



I sincerely express indebtedness to my esteemed and revered guide Prof. Mukta Bhatele for her invaluable guidance, supervision, and encouragement throughout the work. I also thank MP, CT scan, and MRI Center, Jabalpur, for its contribution to the collection of images used. This study would not have been possible if compression researchers did not routinely place their code and papers on the Internet for public access.


  1. 1.
    Etemad K, Doerman D, Chellappa R (1994) Page segmentation using decision integration and wavelet packet basis. In: Proceedings of international conference on pattern recognition, IEEEGoogle Scholar
  2. 2.
    Lalitha YS, Latte MV (2010) Lossless and lossy compression of DICOM images with scalable ROI. IJCSNS Int J Comput Sci Netw Secur 10(7):276–281Google Scholar
  3. 3.
    Al Muhit A (2008) Error resilient transmission of quality scalable images over wireless channels. Digital Signal Processing, 18:588–597 Google Scholar
  4. 4.
    Dr. Janet J, Mohandass D, Meenalosini S (2010) Lossless compression techniques for medical images in telemedicine. 2(7):112. ISSN print: 2076-2739Google Scholar
  5. 5.
    Kivijarvi J, Ojala TA (1998) A comparison of lossless compression methods for medical images. Comput Med Imaging Graph 22:323–339 Google Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  1. 1.CTAGyan Ganga College of TechnologyJabalpurIndia
  2. 2.Digital CommunicationGyan Ganga College of TechnologyJabalpurIndia
  3. 3.Department of Computer ScienceGyan Ganga College of TechnologyJabalpurIndia

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