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In this work we introduce the use of penalized logistic regression (PLR) to the problem of classification of MRI images and automatic detection of Alzheimer’s disease. Classification of sMRI is approached as a large scale regularization problem which uses voxels as input features. We evaluate how differences in sMRI pre-processing steps such as smoothing, normalization and template selection affect the performance of high-dimensional classification methods. In addition, we compared the relative performance of PLR to a different approach based on support vector machines. To study these questions we used data from the Alzheimer Disease Neuroimaging Initiative (ADNI). The ADNI project follows a protocol consisting of acquisition of two images in each session, image correction steps and further evaluation by experts to obtain the optimized images. We evaluated here the impact of this optimization process on the performance of high-dimensional machine learning techniques. READ MORE
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