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Illumination Correction on MR Images

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Abstract

Objective

An important artifact corrupting Magnetic Resonance Images is the rf inhomogeneity, also called bias artifact. This anomaly produces an abnormal illumination fluctuation on the image, due to variations of the device magnetic field. This artifact is particularly strong on images acquired with a device specialized on upper and lower limbs due to their coil configuration. A method based on homomorphic filtering aimed to suppress this artifact was proposed by Guillemaud. This filter has two faults: it doesn’t provide an indication about the cutoff frequency (cf) and introduces another illumination artifact on the edges of the foreground. This work is an improvement to this method because it resolves both problems.

Methods

The experimental setup has been performed on knee images obtained by 5 volunteers and acquired through an Artoscan device using the following parameters: Spin Echo sequence, Repetition time: 980 ms, Echo time: 26 ms, Slice thickness: 4 mm, Flip Angle: 90°.

Results

Two specialists in orthoptics evaluated the results of the proposed approach by examining the restored images and validating the results produced by the filter. A quantitative evaluation has been performed on a manually segmented restored image using the coefficient of variation (cv) measure.

Conclusions

Following the specialists qualitative evaluation, the illuminance of upper and lower peripheral zones results to be enhanced; a loose of contrast can be noted only in few cases. The Bias image exhibits an artifact focused usually on the central part of the foreground. The quantitative evaluation based on cv shows that this index is lowered for all the segmented regions with respect to the original value. The method is automatic and doesn’t require any hypothesis on the tissues. A manual version of the algorithm can be also implemented allowing the physician to choose the preferred cf. In this case the value selected by the method can be considered as a default value.

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References

  1. VanLeemput K, Maes F, Vandermeulen D, Suetens P (1999) Automated model-based bias field correction of MR images of the brain. IEEE Trans Med Imaging 18:885–896

    Article  CAS  PubMed  Google Scholar 

  2. Guillemaud R, Brady M (1997) Estimating the bias field of MR images. IEEE Trans Med Imaging 16:238–251

    Article  CAS  PubMed  Google Scholar 

  3. Wells WM, Grimson WEL, Kikins R, Jolez FA (1996) Adaptive segmentation of MRI data. IEEE Trans Med Imaging 15:429–442

    Article  CAS  PubMed  Google Scholar 

  4. Ahmed MN, Yamany SM, Mohamed N (2002) A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans Med Imaging 21:193–199

    Article  PubMed  Google Scholar 

  5. Lei Jiang, Wenhui Yang. A Modified Fuzzy C-Means Algorithm for Segmentation of Magnetic Resonance Images. Proc. VIIth Digital Image Computing: Techniques and Applications. Sun C, Talbot H, Ourselin S, Adriaansen T. Editions. 2003; 225-31

  6. Pham DL, Prince JL (1999) Adaptive fuzzy segmentation of magnetic resonance images. IEEE Trans Med Imaging. 18(9):737–752

    Article  CAS  PubMed  Google Scholar 

  7. Pham DL, Prince JL (1999) An adaptive fuzzy c-means algorithm for image segmentation in the presence of intensity inhomogeneities. Pattern Recogn Lett 20(1):57–68

    Article  Google Scholar 

  8. Likar B, Viergever MA, Pernus F (2001) Retrospective correction of MR intensity inhomogeneity by information minimization. IEEE Trans Med Imaging 20:1398–1410

    Article  CAS  PubMed  Google Scholar 

  9. Laia S-H, Fangb M (2003) A dual image approach for bias field correction in magnetic resonance imaging. Magn Reson Imaging 21:121–125

    Article  Google Scholar 

  10. Dawant BM, Zijdenbos AP, Margolin RA (1993) Correction of intensity variations in mr images for computer-aided tissue classification. IEEE Trans Med Imaging 12:770–781

    Article  CAS  PubMed  Google Scholar 

  11. Axel L, Costantini J, Listerud J (1987) Intensity correction in surface coil MR imaging. Am J Roentgenol 148:418–420

    CAS  Google Scholar 

  12. Tincher M, Meyer CR, Gupta R, Williams DM (1993) Polynomial modelling and reduction of RF body coil spatial inhomogeneity in MRI. IEEE Trans Med Imaging 12:361–365

    Article  CAS  PubMed  Google Scholar 

  13. Brinkmann BH, Manduca A, Robb RA (1998) Optimized homomorphic unsharp masking for MR grayscale inhomogeneity correction. IEEE Trans Med Imaging 17:161–171

    Article  CAS  PubMed  Google Scholar 

  14. Arnold JB, Liow J-S, Schaper KS, Stern JJ, Sled JG, Shattuck DW, Worth AJ, Cohen MS, Leahy RM, Mazziotta JC, Rottenberg DA (2001) Quantitative and qualitive evaluation of six algorithms for correcting intensity non-uniformity effects. Neuroimage 13(5):931–943

    Article  CAS  PubMed  Google Scholar 

  15. Guillemaud R (1998) Uniformity correction with Homomorphic filtering on region of interest. IEEE Int Conf Image Process 2:872–875

    Google Scholar 

  16. Bezdek JC. Pattern Recognition with Fuzzy Objective Function. Plenum Press, 1981.

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Acknowledgments

This work has been partially supported by Istituto Radiologico PIETRO CIGNOLINI – Policlinico dell’Universitá di Palermo. Particulars thanks to Eng. Daniele Peri for his technical support, Dr. Gian Piero De Luca and Dr. Claudio Cusumano for their medical support and Dr. Prof. Giuseppe De Maria for his availability.

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Correspondence to Orazio Gambino PhD.

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Ardizzone E, Pirrone R, Gambino O. Illumination correction on MR images.

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Ardizzone, E., Pirrone, R. & Gambino, O. Illumination Correction on MR Images. J Clin Monit Comput 20, 391–398 (2006). https://doi.org/10.1007/s10877-006-9040-1

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  • DOI: https://doi.org/10.1007/s10877-006-9040-1

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