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Information Sciences: an International Journal
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The study of neurodegenerative diseases has been based for some time on visual and semi-quantitative analysis of medical imaging. This is the case of Parkinsonian Syndrome (PS) or Parkinsonism, which is the second most common neurodegenerative disorder, where ^1^2^3I-ioflupane (better known by its tradename, DaTSCAN) images have been of great help. Recently, new developments in machine learning methods and statistics have been applied to the analysis of medical images, yielding to a more operator-independent, objective analysis of them, and thus, setting the Computer Aided Diagnosis (CAD) paradigm. In this work, a new CAD system based on preprocessing, voxel selection, feature extraction and classification of the images is proposed. After preprocessing the images, voxels are ranked by means of their significance in class discrimination, and the first N are selected. Then, these voxels are modelled using Independent Component Analysis (ICA), obtaining a few components that represent each image, which will be used later to train a classifier. The proposed system has been tested on two databases: a 208-DaTSCAN image database from the ''Virgen de la Victoria'' Hospital in Malaga (VV), Spain and a 289-DaTSCAN image database from the Parkinson Progression Markers Initiative (PPMI). Values of accuracy up to 94.7% and 91.3% for VV and PPMI databases are achieved by the proposed system, which has proved its robustness in PS pattern detection, and significantly improves the baseline Voxels-as-Features (VAF) approach, used as an approximation of the visual analysis.