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Clinical magnetic resonance imaging (MRI) data is normally corrupted by random noise from the measurement process which reduces the accuracy and reliability of any automatic analysis. For this reason, denoising methods are often applied to increase the : Signal-to-Noise Ratio (SNR) and improve image quality. The search for efficient image denoising methods is still a valid challenge at the crossing...
Electrical Impedance Tomography (EIT) calculates the internal conductivity distribution within a body using electrical contact measurements. Conventional EIT reconstruction methods solve a linear model by minimizing the least squares error, i.e., the Euclidian or L2-norm, with regularization. Recently, total variation and L1 regularization have become more popular in medical image reconstruction....
Wave atom transform is a new multi-resolution technique, which has the ability to adapt to arbitrary local directions of a pattern, and to sparsely represent anisotropic patterns aligned with the axes. In this paper, a de-noising technique is proposed to remove the rician noise from Magnetic Resonance Images using wave atom shrinkage. It is well known that the noise in magnetic resonance imaging has...
This paper presents a generalization of the Bowsher prior for SPECT reconstruction using anatomical prior. Instead of considering a binary selection of the neighbors of each reconstructed voxel based on the anatomical prior values, each neighbors are taken into account with a suitable weight. We tried three different weights. We showed that, for brain SPECT reconstruction using MRI, in the case of...
Magnetic resonance (MR) is a typical medical imaging technique. It can provide high resolution 3D image with anatomical and function information through analyzing MRI sequence, which facilitates and improves diagnosis and patient treatment. The first important step in image analysis is image segmentation. In this paper, numerous methods that have been developed for segmentation in MRI are reviewed...
Functional Magnetic Resonance (fMRI) data is most often analyzed using linear regression type methods that consider each voxel separately or by using exploratory methods such as Principal Component Analysis (PCA) or Independent Component Analysis (ICA). In this paper we introduce a model, which we call XnPCA, that combines regression with PCA. Unlike the linear regression methods XnPCA allows for...
In magnetic resonance imaging (MRI), accuracy of brain structures quantification may be affected by the partial volume (PV) effect. PV is due to the limited spatial resolution of MRI compared to the size of anatomical structures. When considering the cortex, measurements can be even more difficult as it spans only a few voxels. In tight sulci areas, where the two banks of the cortex are in contact,...
A new fusion algorithm for MRI and color images based on calculating mutual information in wavelet domain has been proposed. Usually mutual information in an image fusion algorithm is calculated in time/space domain. The new algorithm calculates mutual information in wavelet domain based on the local properties of wavelet transformation, which will be used as criterion to select fusion strategies...
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