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Engineering Commons

Open Access. Powered by Scholars. Published by Universities.®

2014

Western University

Algorithms

Articles 1 - 2 of 2

Full-Text Articles in Engineering

Detection Of Temporal Lobe Epilepsy Using Support Vector Machines In Multi-Parametric Quantitative Mr Imaging., Diego Cantor-Rivera, Ali R Khan, Maged Goubran, Seyed M Mirsattari, Terry M Peters Jan 2014

Detection Of Temporal Lobe Epilepsy Using Support Vector Machines In Multi-Parametric Quantitative Mr Imaging., Diego Cantor-Rivera, Ali R Khan, Maged Goubran, Seyed M Mirsattari, Terry M Peters

Robarts Imaging Publications

The detection of MRI abnormalities that can be associated to seizures in the study of temporal lobe epilepsy (TLE) is a challenging task. In many cases, patients with a record of epileptic activity do not present any discernible MRI findings. In this domain, we propose a method that combines quantitative relaxometry and diffusion tensor imaging (DTI) with support vector machines (SVM) aiming to improve TLE detection. The main contribution of this work is two-fold: on one hand, the feature selection process, principal component analysis (PCA) transformations of the feature space, and SVM parameterization are analyzed as factors constituting a classification …


Stationary Wavelet Transform For Under-Sampled Mri Reconstruction., Mohammad H Kayvanrad, A Jonathan Mcleod, John S H Baxter, Charles A Mckenzie, Terry M Peters Jan 2014

Stationary Wavelet Transform For Under-Sampled Mri Reconstruction., Mohammad H Kayvanrad, A Jonathan Mcleod, John S H Baxter, Charles A Mckenzie, Terry M Peters

Robarts Imaging Publications

In addition to coil sensitivity data (parallel imaging), sparsity constraints are often used as an additional lp-penalty for under-sampled MRI reconstruction (compressed sensing). Penalizing the traditional decimated wavelet transform (DWT) coefficients, however, results in visual pseudo-Gibbs artifacts, some of which are attributed to the lack of translation invariance of the wavelet basis. We show that these artifacts can be greatly reduced by penalizing the translation-invariant stationary wavelet transform (SWT) coefficients. This holds with various additional reconstruction constraints, including coil sensitivity profiles and total variation. Additionally, SWT reconstructions result in lower error values and faster convergence compared to DWT. These concepts …