Paper Info
Title
Ensemble-classifiers-assisted detection of cerebral microbleeds in brain MRI.
Abstract
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.
Year
DOI
Venue
2018
10.1016/j.compeleceng.2018.02.021
Computers & Electrical Engineering
Keywords
Field
DocType
Cerebral Microbleeds,Support Vector Machine,Quadratic Discriminant Analysis,Ensemble classifier,Susceptibility-Weighted Imaging
Brain mri,Pattern recognition,Computer science,Support vector machine,Filter (signal processing),Real-time computing,Feature extraction,Artificial intelligence,Classifier (linguistics),False positive paradox,Quadratic classifier
Journal
Volume
ISSN
Citations 
69
0045-7906
7
PageRank 
References 
Authors
0.51
9
10
Name
Order
Citations
PageRank
Tayyab Ateeq170.51
Muhammad Nadeem Majeed270.84
Anwar, S.311816.48
Muazzam Maqsood4419.65
Zahoor ur Rehman5243.88
Jong Weon Lee67312.70
Khan Muhammad798667.67
Shuihua Wang8156487.49
Sung Wook Baik996057.77
Irfan Mehmood1052230.84