0000000001151419
AUTHOR
Olivier Salvado
Automatic Detection of Cerebral Microbleed in SWI Using Radon Transform
International audience; 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.
A Machine Learning Approach for Computer-Aided Detection of Cerebral Microbleed Using High-order Shape Features
International audience; This paper presents a novel machine learning approach for computer-aided detection of microbleeds in SWI. The major contributions are: identifying microbleed extent in order to extract proper cubic regions-of-interest (ROI) containing the structure, (2) extracting a set of robust 3- dimensional (3D) Radon- and Hessian-based shape descriptors within the ROIs as well as 2D Radon features computed on intensity-projection images of the corresponding ROIs, and (3) incorporating a cascade of random forests (RF) classifiers to iteratively reduce false detection rates while maintaining a high sensitivity.