Conference paper


Advances in biomedical imaging technology have brought about the possibility of non-invasive scanning the structures of the internal organs and examining their behavior in healthy and disease states. 2D/3D ultrasound imaging, high resolution multi-slice computed tomography, and endoscopic imaging provide valuable information about the functioning of the human body. These advances have made the medical imaging an essential component in many fields of biomedical research such as generating 3D reconstructions of viruses from micrographs or studying regional metabolic brain activities. Clinical practice also benefits from the data provided by biomedical imaging modalities. Detection and diagnosis of cancer for instance, is carried out by using multi-slice computer tomography or magnetic resonance imaging. These benefits on the other hand, have triggered an explosion in the amount of biomedical images obtained daily from the image acquisition modalities. Automatic processing and interpretation of these images through image processing methods therefore has become unavoidable. The analysis and interpretation of complicated or unexpected results require deep understanding of the underlying theory and methods involved in image processing beside the medical physics. Biomedical image processing is an interdisciplinary field combining biomedical engineering and computer science. The first class of image processing operations for biomedical application are the fundamental techniques intended to improve the accuracy of the information obtained from the imaging modality. These techniques which include adjusting the brightness and contrast of the image, reduce image noise and correcting for imaging artifacts, generally involves only basic arithmetic operations.


Gray Level Seed Point Biomedical Image Image Processing Operation High Frequency Content 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Cho ZH, Jones JP, and Singh M. Foundations of Medical Imaging. Wiley, New York, NY, 1993.Google Scholar
  2. 2.
    Huda W and Slone R. Review of Radiologic Physics. Williams and Wilkins, Baltimore, MD, 1995.Google Scholar
  3. 3.
    Huettel S.A., Song A.W., and McCarthy G., Functional Magnetic Resonance Imaging, Sinauer Associates, 2004.Google Scholar
  4. 4.
    Herman, G. T., Fundamentals of computerized tomography: Image reconstruction from projection, 2nd edition, Springer, 2009.Google Scholar
  5. 5.
    Novelline, Robert, Squire’s Fundamentals of Radiology, 5th edition, Harvard University Press, 1997.Google Scholar
  6. 6.
    Gonzalez R.C. and Woods R.E. Digital Image Processing. Prentice Hall, 3rd edition, Upper Saddle River, NJ, 2007.Google Scholar
  7. 7.
    Sezgin M. and Sankur B., Survey over image thresholding techniques and quantitative performance evaluation, Journal of Electronic Imaging 13(1), 146–165, 2004.CrossRefGoogle Scholar
  8. 8.
    Haralick R. and Shapiro L. Computer and Robot Vision, Vol. 1, Addison-Wesley Publishing Company, 1992.Google Scholar
  9. 9.
    Ballard D. H., Brown C. M., Computer Vision, Prentice Hall; first edition, 1982.Google Scholar
  10. 10.
    Sethian J., Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science, Cambridge University Press, Cambridge, UK, 2003.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.AnkaraTurkey

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