EFFICIENT MACHINE LEARNING FRAMEWORK FOR COMPUTER-AIDED DETECTION OF CEREBRAL MICROBLEEDS USING THE RADON TRANSFORM
| Affiliation auteurs | !!!! Error affiliation !!!! |
| Titre | EFFICIENT MACHINE LEARNING FRAMEWORK FOR COMPUTER-AIDED DETECTION OF CEREBRAL MICROBLEEDS USING THE RADON TRANSFORM |
| Type de publication | Conference Paper |
| Year of Publication | 2014 |
| Auteurs | Faziollahi A, Meriaudeau F, Villemagne VL, Rowe CC, Yates P, Salvadol O, Bourgeat P, Grp AIBLRes |
| Conference Name | 2014 IEEE 11TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI) |
| Publisher | IEEE; IEEE Engn Med & Biol Soc; IEEE Signal Proc Soc; EGI; GE; Kitware |
| Conference Location | 345 E 47TH ST, NEW YORK, NY 10017 USA |
| ISBN Number | 978-1-4673-1961-4 |
| Mots-clés | Cerebral Microbleeds, Multi-scale Laplacian of Gaussian, Radon transform, sphere detection, Susceptibility Weighted Imaging |
| Résumé | Recent developments of susceptibility weighted MR techniques have improved visualization of venous vasculature and underlying pathologies such as cerebral microbleed (CMB). CMBs are small round hypointense lesions on MRI images that are emerging as a potential biomarker for cerebrovascular disease. CMB manual rating has limited reliability, is time-consuming and is prone to errors as small CMBs can be easily missed or mistaken for venous cross-sections. This paper presents a computer-aided detection technique that utilizes a novel cascade of random forest classifiers which are trained on robust Radon-based features with an unbalanced sample distribution. The training samples and their associated bounding box were acquired from a multi-scale Laplacian of Gaussian technique with respect to their geometric characteristics. Validation results demonstrate that the current approach outperforms state of the art approaches with sensitivity of 92.04% and an average false detection rate of 16.84 per subject. |