Abstract #1956
A Machine Learning Approach for Computer-Aided Detection of Cerebral Microbleed Using High-order Shape Features
Amir Fazlollahi 1,2 , Fabrice Meriaudeau 2 , Luca Giancardo 3 , Christopher C. Rowe 4 , Victor L. Villemagne 4 , Paul Yates 4 , Olivier Salvado 1 , Pierrick Bourgeat 1 , and on behalf of the AIBL Research Group 5
1
CSIRO Preventative Health Flagship, CSIRO
Computational Informatics, Brisbane, QLD, Australia,
2
Le2i,
University of Burgundy, Le Creusot, France,
3
RLE,
Massachusetts Institute of Technology, MA, United
States,
4
Department
of Nuclear Medicine and Centre for PET, Austin Hospital,
Melbourne, VIC, Australia,
5
http://www.aibl.csiro.au/,
Australia
Since the incidence of cerebral microbleeds (CMBs) have
come to attention as a potential biomarker of
cerebrovascular disease and dementia, a computer-aided
detection scheme to improve screening is required. In
this work, a novel approach of CMB detection in SWIs is
presented and compared to visual rating. The proposed
method (1) identifies potential CMB candidates with
their corresponding bounding boxes using a multi-scale
Gaussian technique, (2) extracts a set of robust 3D
Radon- and Hessian-based shape descriptors within the
bounding boxes, as well as 2D Radon features computed on
intensity-projection images, and (3) incorporates a
cascade of random forests classifiers to reduce false
detection rates.
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