EFFICIENT MACHINE LEARNING FRAMEWORK FOR COMPUTER-AIDED DETECTION OF CEREBRAL MICROBLEEDS USING THE RADON TRANSFORM

Amir Fazlollahi (1) , Fabrice Meriaudeau (1) , Victor L Villemagne (2, 3) , Christopher Rowe (4, 5) , Paul Yates (4) , Olivier Salvado (6) , Pierrick T. Bourgeat (6)

1 Le2i - Laboratoire Electronique, Informatique et Image [UMR6306]
2 Department of Nuclear Medicine
3 The Mental Health Research Institute
4 Department of Nuclear Medicine
5 Department of Medecine
6 CSIRO ICT Centre - CSIRO Information and Commuciation Technologies

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 crosssections. 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 multiscale 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.

Domaines: Traitement des images [eess.IV]

Amir Fazlollahi, Fabrice Meriaudeau, Victor L Villemagne, Christopher Rowe, Paul Yates, et al.. EFFICIENT MACHINE LEARNING FRAMEWORK FOR COMPUTER-AIDED DETECTION OF CEREBRAL MICROBLEEDS USING THE RADON TRANSFORM. ISBI'2014, Apr 2014, Beijing, China. pp.11"-116. ⟨hal-00989915⟩