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Abstrakty
The paper presents a new method of removing the noisy background from the sequence of magnetic resonance imaging (MRl) scans. The sequence of scans is required in order to monitor a passage of a contrast agent through the brain tissue. The scans contain the noisy head-cross data and also the noisy background data. The latter has to be removed and excluded from a further analysis. It is achieved by applying some basic morphological operations to the previously binarized MRl scans. The results of separating the background from the sequence of scans are presented in the paper. The scans binarization method is described and compared with the widely used Otsu method. The proposed method of the noisy background separation is easily applicable, efficient and does not need any sophisticated calculations.
Wydawca
Czasopismo
Rocznik
Tom
Strony
15--27
Opis fizyczny
Bibliogr. 19 poz., rys., wykr.
Twórcy
autor
autor
- Faculty of Electronics, Telecommunication and Informatics, Gdańsk University of Technology, 80-952 Gdańsk, Poland, ul. Narutowicza 11/12, renatak@biomed.eti.pg.gda.pl
Bibliografia
- 1. Rumiński J., Kalicka R., Bobek-Billewicz B.: Parametric imaging in brain studies with the use of MRI and PET. (in Polish) Wydawnictwo Gdańskie, Gdańsk 2006.
- 2. Sorensen G.A., Reimer P.: Cerebral MR Perfusion Imaging.Georg Thieme Verlag, Stuttgart, 2000.
- 3. Kalicka R, Pietrenko-Dąbrowska A.: Parametric Modeling of DSC-MRI Data with Stochastic Filtration and Optimal Input Design Versus Non-Parametric Modeling, Ann. Biomed. Eng., 2007, March, 3, 453-464.
- 4. Kalicka R., A. Pietrenko-Dąbrowska A., Lipiński S., Nowicki R.: Brain perfusion imaging with the use of parametric modelling basing on DSC-MRI data. Proc. 1st Intern. Conf. Inform. Technol., Gdańsk 2008.
- 5. Awate P.S., Whitaker R.T.: Feature-preserving MRI denoising: A nonparametric empirical Bayes approach. IEEE Trans. Med. Imag., 2007, 29, 9, 1242-1255.
- 6. Behzadi Y., Restom K., Liau J., Liu T.T.: A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. Neuro Image, 2007, 37, 90-101.
- 7. Lysaker M., Lundervold A., Tai X.: Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time. IEEE Trans. Image Proc., 2003, 12, 1579-1590.
- 8. Pizurica A., Wink A.M., Vansteenkiste E., Philips W., Roerdink J. B.T.M: A review of wavelet denoising in MRI and ultrasound brain imaging. Curr. Med. Imag. Rev. 2006, 2, 2, 247-260.
- 9. Kalicka R., Lipiński S.: Valuation of usefulness of Kalman filtration to improve noise properties of DSC-MRI brain research data. (in Polish) Pomiary Automatyka Kontrola 2008, 3, 118-121.
- 10. Salluzzi M., Frayne R., Smith M.R.: Is correction necessary when clinically determining quantitative cerebral perfusion parameters from multi-slice dynamic susceptibility contrast MR studies? Physics in Medicine and Biology 2006, 51, 407-424.
- 11. Smith M.R., Lu H., Frayne R.: Signal-to-noise ratio effects in quantitative cerebral perfusion using dynamic susceptibility contrast agents. Magn. Reson. Med. 2003, 49, 122-128.
- 12. Sezgin M., Sankur B.: Survey over image thresholding techniques and quantitative performance evaluation. J. Electr. Imag. 2004, 13, 1, 146-165.
- 13. Solihin Y., Leedham C.G.: The multi-stage approach to grey-scale image thresholding for specific applications. Internet site address: http://www.ece.ncsu.edu/arpers/.
- 14. Wu S., Amin A.: Automatic thresholding of gray-level using multi-stage approach. Proc. Seventh Intern. Conf. Doc. Anal. Rec. 2003, 1, 493-497.
- 15. Antoine Ch., Lloyd M., Antoine J.: A robust thresholding algorithm for halftone dots. J. Pulp Paper Sc. 2001, 27, 8.
- 16. Lin K. Ch.: On improvement of the computation speed of Otsu's image thresholding, J. Elec. Imag. 2005, 14, 2.
- 17. Xianpeng L., Feng Z., Yingming H., Jinjun O.: Integral Image Based Fast Algorithm for Two-Dimensional Otsu Thresholding. Congr. Image Signal Proc. 2008, 3, 677-681.
- 18. Shapiro L., Stockman G.: Computer vision, Upper Saddle River, Prentice Hall 2001.
- 19. Tadeusiewicz R., Korohoda P.: Computer analysis and image processing. (in Polish) Wydawnictwo Fundacji Postępu Telekomunikacji, Kraków, 1997.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPZ1-0059-0012
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