Ensemble-classifiers-assisted detection of cerebral microbleeds in brain MRI

Cerebral Microbleeds (CMBs) are considered as an essential indicator in the diagnosis of critical cerebrovascular diseases such as ischemic stroke and dementia. Manual detection of CMBs is prone to errors due to complex morphological nature of CMBs. In this paper, an efficient method is presented for CMB detection in Susceptibility-Weighted Imaging (SWI) scans. The proposed framework consists of three phases: i) brain extraction, ii) extraction of initial candidates based on threshold and size based filtering, and iii) feature extraction and classification of CMBs from other healthy tissues in order to remove false positives using Support Vector Machine, Quadratic Discriminant Analysis (QDA) and ensemble classifiers. The proposed technique is validated on a dataset of 20 subjects with CMBs that consists of 14 subjects for training and 6 subjects for testing. QDA classifier achieved the best sensitivity of 93.7% with 56 false positives per patient and 5.3 false positives per CMB.