TY - GEN
T1 - Compressive Sensing for Three-Dimensional Brain Magnetic Resonance Imaging
AU - D’souza, Selrina
AU - Anitha, H.
AU - Kotegar, Karunakar
N1 - Publisher Copyright:
© Springer Nature Singapore Pte Ltd. 2019.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Three dimensional (3D) Magnetic Resonance Imaging (MRI) reconstructions depend heavily on the imaging speed. Magnetic Resonance (MR) images consist of large volume of redundant and sparse data. Therefore, the need to reduce this data without degrading the image information. In Fourier Domain, sparse nature of MR images enables image reconstruction with fewer Fourier coefficients. Fourier Transform (FT) maps the image into the frequency domain using fixed and same size window throughout the analysis. In our paper, a method to perform compressive sensing for MR image is presented. Anisotropic filtering using Active Contour Modelling is performed to smoothen the image in order to preserve edge information. MR image is converted into Fourier Domain using Discrete Fourier Transform (DFT). l1 and l2 reconstruction algorithms are used to reconstruct the images using minimum coefficients that have maximum information.
AB - Three dimensional (3D) Magnetic Resonance Imaging (MRI) reconstructions depend heavily on the imaging speed. Magnetic Resonance (MR) images consist of large volume of redundant and sparse data. Therefore, the need to reduce this data without degrading the image information. In Fourier Domain, sparse nature of MR images enables image reconstruction with fewer Fourier coefficients. Fourier Transform (FT) maps the image into the frequency domain using fixed and same size window throughout the analysis. In our paper, a method to perform compressive sensing for MR image is presented. Anisotropic filtering using Active Contour Modelling is performed to smoothen the image in order to preserve edge information. MR image is converted into Fourier Domain using Discrete Fourier Transform (DFT). l1 and l2 reconstruction algorithms are used to reconstruct the images using minimum coefficients that have maximum information.
UR - https://www.scopus.com/pages/publications/85069745466
UR - https://www.scopus.com/pages/publications/85069745466#tab=citedBy
U2 - 10.1007/978-981-13-9184-2_26
DO - 10.1007/978-981-13-9184-2_26
M3 - Conference contribution
AN - SCOPUS:85069745466
SN - 9789811391835
T3 - Communications in Computer and Information Science
SP - 294
EP - 302
BT - Recent Trends in Image Processing and Pattern Recognition - 2nd International Conference, RTIP2R 2018, Revised Selected Papers
A2 - Santosh, K.C.
A2 - Hegadi, Ravindra S.
PB - Springer Verlag
T2 - 2nd International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2018
Y2 - 21 December 2018 through 22 December 2018
ER -