Advertisement

Frequency Determined Homomorphic Unsharp Masking Algorithm on Knee MR Images

  • Edoardo Ardizzone
  • Roberto Pirrone
  • Orazio Gambino
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)

Abstract

A very important artifact corrupting Magnetic Resonance (MR) Images is the RF inhomogeneity, also called Bias artifact. The visual effect produced by this kind of artifact is an illumination variation which afflicts this kind of medical images. In literature a lot of works oriented to the suppression of this artifact can be found. The approaches based on homomorphic filtering offer an easy way to perform bias correction but none of them can automatically determine the cut-off frequency. In this work we present a measure based on information theory in order to find the frequency mentioned above and this technique is applied to MR images of the knee which are hardly bias corrupted.

Keywords

IEEE Transaction Medical Image Corrected Bias Illumination Variation Degradation Model 
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.

References

  1. 1.
    Styner, M., Brechbuhler, C., Szckely, G., Gerig, G.: Parametric estimate of intensity inhomogeneities applied to MRI Medical Imaging. IEEE Transactions on Medical Imaging 22, 153–165 (2000)CrossRefGoogle Scholar
  2. 2.
    Laia, S.-H., Fangb, M.: A dual image approach for bias field correction in magnetic resonance imaging. Magnetic Resonance Imaging 21, 121–125 (2003)CrossRefGoogle Scholar
  3. 3.
    Ahmed, M.N., Yamany, S.M., Mohamed, N.: A Modified Fuzzy C-Means Algorithm for Bias Field Estimation and Segmentation of MRI Data. IEEE Transactions on Medical Imaging 21, 193–199 (2002)CrossRefGoogle Scholar
  4. 4.
    Van Leemput, K., Maes, F., Vandermeulen, D., Suetens, P.: Automated Model-Based Bias Field Correction of MR Images of the Brain. IEEE Transactions on Medical Imaging 18, 885–896 (1999)CrossRefGoogle Scholar
  5. 5.
    Guillemaud, R.: Uniformity Correction with Homomorphic filtering on Region of Interest. In: IEEE International Conference on Image Processing, vol. 2, pp. 872–875 (1998)Google Scholar
  6. 6.
    Guillemaud, R., Brady, M.: Estimating the Bias Field of MR Image. In: IEEE International Conference on Image Processing, vol. 2, pp. 872–875 (1998)Google Scholar
  7. 7.
    Dawant, B.M., Zijdenbos, A.P., Margolin, R.A.: Correction of Intensity Variations in MR Images for Computer-Aided Tissue Classification. IEEE Transactions on Medical Imaging 12, 770–781 (1993)CrossRefGoogle Scholar
  8. 8.
    Axel, L., Costantini, J., Listerud, J.: Intensity Correction in Surface Coil MR Imaging. American Journal on Roentgenology 148, 418–420 (1987)Google Scholar
  9. 9.
    Jiang, L., Yang, W.: A Modified Fuzzy C-Means Algorithm for Segmentation of Magnetic Resonance Images. In: Sun, C., Talbot, H., Ourselin, S., Adriaansen, T. (eds.) Proc. VIIth Digital Image Computing: Techniques and Applications, pp. 225–231 (2003)Google Scholar
  10. 10.
    Tincher, M., Meyer, C.R., Gupta, R., Williams, D.M.: Polynomial Modelling and Reduction of RF Body Coil Spatial Inhomogeneity in MRI. IEEE Transactions on Medical Imaging 12, 361–365 (1993)CrossRefGoogle Scholar
  11. 11.
    Brinkmann, B.H., Manduca, A., Robb, R.A.: Optimized Homomorphic Unsharp Masking for MR Grayscale Inhomogeneity Correction. IEEE Transactions on Medical Imaging 17, 161–171 (1998)CrossRefGoogle Scholar
  12. 12.
    Wells, W.M., Grimson, W.E.L., Kikins, R., Jolez, F.A.: Adaptive Segmentation of MRI Data. IEEE Transactions on Medical Imaging 15, 429–442 (1996)CrossRefGoogle Scholar
  13. 13.
    Likar, B., Viergever, M.A., Pernus, F.: Retrospective Correction of MR Intensity Inhomogeneity by Information Minimization. IEEE Transactions on Medical Imaging 20, 1398–1410 (2001)CrossRefGoogle Scholar
  14. 14.
    Pham, D.L., Prince, J.L.: Adaptive Fuzzy Segmentation of Magnetic Resonance Images. IEEE Transactions on Medical Imaging 18(9), 737–752 (1999)CrossRefGoogle Scholar
  15. 15.
    Pham, D.L., Prince, J.L.: An Adaptive Fuzzy C-Means Algorithm for Image Segmentation in the Presence of Intensity Inhomogeneities. Pattern Recognition Letters 20(1), 57–68 (1999)zbMATHCrossRefGoogle Scholar
  16. 16.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Prentice-Hall, Englewood CliffsGoogle Scholar
  17. 17.
    Arnold, J.B., Liow, J.-S., Schaper, K.S., Stern, J.J., Sled, J.G., Shattuck, D.W., Worth, A.J., Cohen, M.S., Leahy, R.M., Mazziotta, J.C., Rottenberg, D.: Quantitative and Qualitive Evaluation of Six Algorithms for Correcting Intensity Non-Uniformity Effects. Neuroimage 13(5), 931–943 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Edoardo Ardizzone
    • 1
  • Roberto Pirrone
    • 1
  • Orazio Gambino
    • 1
  1. 1.Computer Science and Artificial Intelligence LaboratoryUniversitá degli Studi di PalermoPalermoItaly

Personalised recommendations