NDLI: Study of denoising algorithm for MR image based on Contourlet Transform

Content Provider	IEEE Xplore Digital Library
Author	Lei Guo Guizhi Xu Lei Zhao Ming Hu Ying Li
Copyright Year	2012
Description	Author affiliation: Province-Ministry Joint Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability, Hebei University of Technology, Tianjin 300130, China (Lei Guo; Guizhi Xu; Ming Hu; Ying Li) \|\| Department of Radiology, Harvard Medical School and Brigham & Women's Hospital, Boston, MA 02115, USA (Lei Zhao)
Abstract	It is more difficult to remove the noise and keep the edge information in the same time in the brain MR image than the ordinary image since the boundaries of encephalic tissue are highly complicated. As one kind of the multi-scale and multi-directional geometrical analysis method, the Contourlet Transform (CT) is an optimal representation of the contour and texture information in an image and overcomes the drawbacks of the Wavelet Transform (WT). Due to the lack of translation invariance of the CT, a Cycle Spinning (CS) technique was employed to smooth Gibbs-like artifacts. In this study, Combined the CT and the CS, the CTCS with different threshold for each direction of each scale was investigated to remove the noise in the brain MR image. Theoretical analysis and experimental results showed that the CTCS is outperforms the CT and the WT both visually and in terms of the SNR.
Starting Page	17
Ending Page	20
File Size	1888266
Page Count	4
File Format	PDF
ISBN	9781457717581
ISSN	19449429
e-ISBN	9781457717598
DOI	10.1109/VECIMS.2012.6273177
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-02
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computed tomography Noise reduction Transforms Signal to noise ratio Speckle Gaussian noise cycle spinning medical image processing denoising algorithm contourlet transform
Content Type	Text
Resource Type	Article

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