Compressive Sensing for Three-Dimensional Brain Magnetic Resonance Imaging

Desouza, Selrina and Anitha, H (2018) Compressive Sensing for Three-Dimensional Brain Magnetic Resonance Imaging. In: International Conference on Recent Trends in Image Processing & Pattern Recognition, 21/12/2018, Solapur.

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Three dimensional (3D) Magnetic Resonance Imaging (MRI) reconstructions depend heavily on the imaging speed is important. Magnetic Resonance (MR) images consist of a 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 in image reconstruction with fewer Fourier coefficients. Fourier Transform 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.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Magnetic Resonance Imaging (MRI), Compressive Sensing (CS), Discrete Fourier Transform (DFT)
Subjects: Engineering > MIT Manipal > Electronics and Communication
Engineering > MIT Manipal > MCA
Depositing User: MIT Library
Date Deposited: 11 Jan 2019 09:26
Last Modified: 11 Jan 2019 09:26

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