MATHEMATICAL MODELING
DATA ANALYSIS
M.S. Usanov, N.S. Kulberg, S.P. Morozov Usage of adaptive homomorphic filters for CT processing
INTELLIGENT SYSTEMS
DISTRIBUTED SYSTEMS
REGULATORY FRAMEWORK OF AUTOMATED SYSTEMS SYNTHESIS
M.S. Usanov, N.S. Kulberg, S.P. Morozov Usage of adaptive homomorphic filters for CT processing

Abstract.

The article deals with effective usage of homomorphic filtering in the processing of data that do not comply with Gaussian distribution law. As an example, we used data from computed tomography. Data processing is based on wavelet – filtering, including noise reduction and edge enhancement for objects of interest. Results have shown significant data quality enhancement. What’s more important, we managed to avoid some unacceptable artifacts that occur when processed without the suggested transformation.

Keywords:

computed Tomography, Image Processing, Nonlinear Transform, Edge Enhancement, Noise Reduction Inverse Transform Sampling, Homomorphic Filters

PP. 33-42.

REFERENCES

1. Matias Prokop, Michael Galanski. Spiral and Multislice Computed Tomography of the body: Handbook; Thieme; 1st edition (2001), pp. 14-16.
2. G. Hendeby, F. Gustafsson. Fundamental filtering limitations in linear non-Gaussian systems 16th Triennial IFAC World Congress, 2005.
3. Hyvärinen A., Oja E. Independent component analysis: algorithms and applications. Neural networks. 2000;13(4), pp. 411-30.
4. Gruzman I. S. Two-Phase Methods of the Primary Processing of the Cluttered Multidimensional Signals and Images. Doctoral Thesis. Novosibirsk State Technical University, 1997.
5. Arakeri M. P., et al., editors. A comparative performance evaluation of independent component analysis in medical image denoising. ICRTIT, 2011.
6. Kämpfe T., Nattkemper T.W., Ritter H. Combining independent component analysis and self-organizing maps for cell image classification. Pattern Recognition: Springer; 2001. p. 262-268.
7. Nishii T., Kono A. K., Tani W. etc. Four-dimensional noise reduction using the time series of medical computed tomography datasets with short interval times: a static-phantom study. PEERJ, Feb. 2016, doi:10.7717/peerj.1680.
8. Liu X, Primak A. N. et al. Quantitative evaluation of noise reduction algorithms for very low dose renal CT perfusion imaging. Proc. SPIE 7258, Medical Imaging 2009: Physics of Medical Imaging, 72581T (13 March 2009); doi:10.1117/12.813777.
9. Donoho, D. De-noising via soft-thresholding, Technical report 409, Dept. of Statistics, Stanford University, 1992.
10. R. Gonsalez, R. Woods. Digital Image Processing. Prentice Hall, Pearson Education, 2002. ISBN 0-201-18075-8.
11. Kulberg N.S. Novel method of image quality enhancement in Ultrasonic medical imaging. Development and testing. Acoustic
Journal, 2009, vol. 55, № 4–5, p. 526-535 (In Russian).
12. Kulberg N. S., и др. Novel Method of the Noise-Reduction in 3D X-Ray Computed Tomography. Proceedings of the Third International Workshop on Image Mining Theory and Applications, pp. 92-99, Angers, France, May 2010.
13. Feifang Hu and James V. Zidek. The Weighted Likelihood. The Canadian Journal of Statistics / La Revue Canadienne de Statistique. Vol. 30, No. 3 (Sep., 2002), pp. 347-371.
14. Oppenheim A. V. et al. Nonlinear Filtering of Multiplied and Convolved Signals. Proceedings of the IEEE Volume 56 No. 8 August 1968, pages 1264-1291 (русский перевод: Оппенхейм, Шефер, Стокхэм. Нелинейная фильтрация сигналов, представленных в виде произведения и свертки. ТИИЭР, 1968, т. 56, № 8, стр. 5-46.
15. Vadzinskiy R.N. Handbook of probability distribution. – SPb.: Science, 2001, p. 33(In Russian)
 

 

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