DocumentCode :
2211873
Title :
Presenting novel de-noising techniques for brain MRI
Author :
Pérez, Gabriela ; Conci, Arua ; Moreno, Ana Belen ; Hernandez, Johann A.
Author_Institution :
Dept. de Cienc. de la Comput., Univ. Rey Juan Carlos-URJC, Madrid, Spain
fYear :
2012
fDate :
11-13 April 2012
Firstpage :
244
Lastpage :
247
Abstract :
Standard acquisition of MRI presents Rician statistical noise that degrades the performance of other steps of the image analysis. In this paper we present new ways to reduce the noise of brain images in the preprocessing stage. We propose a novel wavelet domain method for noise restoration based on discrete wavelet packets transform (WPT). The developed techniques combine adaptive Wiener filter and soft threshold for the 2D wavelet packet coefficients of the best tree decomposition. The novel presented techniques are compared with the most traditional one considering qualitative and quantitative results. In the comparison the Mean Square Error and the normal cross correlations are considered for a complete set of structural (T1-w) brain MRI. Moreover we show by experiments that the common prior adaptive Wiener filtering often used by many authors is a dispensable procedure.
Keywords :
Wiener filters; biomedical MRI; brain; image denoising; medical image processing; neurophysiology; wavelet transforms; 2D wavelet packet coefficients; Rician statistical noise; WPT; brain MRI denoising techniques; brain image noise reduction; discrete wavelet packets transform; image analysis; mean square error; noise restoration; preprocessing stage; prior adaptive Wiener filtering; soft threshold; tree decomposition; wavelet domain method; Noise; Standards; MRI brain filter; Rician noise; Wiener filter; soft thresholding; wavelet packets transform;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Signals and Image Processing (IWSSIP), 2012 19th International Conference on
Conference_Location :
Vienna
ISSN :
2157-8672
Print_ISBN :
978-1-4577-2191-5
Type :
conf
Filename :
6208118
Link To Document :
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