Pattern recognition and machine learning for magnetic resonance images with kernel methods.
Doctoral thesis, UCL (University College London).
The aim of this thesis is to apply a particular category of machine learning and pattern recognition algorithms, namely the kernel methods, to both functional and anatomical magnetic resonance images (MRI). This work specifically focused on supervised learning methods. Both methodological and practical aspects are described in this thesis. Kernel methods have the computational advantage for high dimensional data, therefore they are idea for imaging data. The procedures can be broadly divided into two components: the construction of the kernels and the actual kernel algorithms themselves. Pre-processed functional or anatomical images can be computed into a linear kernel or a non-linear kernel. We introduce both kernel regression and kernel classification algorithms in two main categories: probabilistic methods and non-probabilistic methods. For practical applications, kernel classification methods were applied to decode the cognitive or sensory states of the subject from the fMRI signal and were also applied to discriminate patients with neurological diseases from normal people using anatomical MRI. Kernel regression methods were used to predict the regressors in the design of fMRI experiments, and clinical ratings from the anatomical scans.
|Title:||Pattern recognition and machine learning for magnetic resonance images with kernel methods|
|Open access status:||An open access version is available from UCL Discovery|
|UCL classification:||UCL > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Neurology > Imaging Neuroscience|
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