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Abstract #0621

Automatic Detection of Cerebral Microbleed in SWI Using Radon Transform

Amir Fazlollahi1, 2, Fabrice Meriaudau2, Luca Giancardo, 23, Patricia M. Desmond4, Victor L. Villemagne5, Christopher C. Rowe5, Paul Yates5, Olivier Salvado1, Bourgeat Pierrick1, the AIBL Research Group6

1The Australian E-Health Research Centre-BioMedIA, Brisbane, QLD, Australia; 2Laboratoire Le2I, Universit de Bourgogne, Le Creusot, France; 3Istituto Italiano di Tecnologia, Genoa, Italy; 4Department of Radiology, The Melbourne Brain Centre at Royal Melbourne Hospital, University of Melbourne, Melbourne, VIC, Australia; 5Department of Nuclear Medicine and Centre for PET, Austin Hospital, Melbourne, VIC, Australia; 6, Australia, Australia

Since presence and number of cerebral microbleeds (CMBs) have come to attention as a potential biomarker, an automated scheme to improve visualization is required. In this work, a new approach of CMB identification in SWIs is presented and compared to visual rating. The method relies on two main steps: a 3D anisotropic multi-scale approach that extracts size and centre of all potential CMBs within the image, and feature extraction using the Radon Transform for final classification using a random forest classifier. The novelty of the technique consists in combining Radon transform and multiscale analysis to obtain robust feature descriptors.