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; 6http://www.aibl.csiro.au/,
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.