title: Brain extraction based on locally linear representation-based classification.
abstract: Brain extraction is an important procedure in brain image analysis. Although numerous brain extraction methods have been presented, enhancing brain extraction methods remains challenging because brain MRI images exhibit complex characteristics, such as anatomical variability and intensity differences across different sequences and scanners. To address this problem, we present a Locally Linear Representation-based Classification (LLRC) method for brain extraction. A novel classification framework is derived by introducing the locally linear representation to the classical classification model. Under this classification framework, a common label fusion approach can be considered as a special case and thoroughly interpreted. Locality is important to calculate fusion weights for LLRC; this factor is also considered to determine that Local Anchor Embedding is more applicable in solving locally linear coefficients compared with other linear representation approaches. Moreover, LLRC supplies a way to learn the optimal classification scores of the training samples in the dictionary to obtain accurate classification. The International Consortium for Brain Mapping and the Alzheimer's Disease Neuroimaging Initiative databases were used to build a training dataset containing 70 scans. To evaluate the proposed method, we used four publicly available datasets (IBSR1, IBSR2, LPBA40, and ADNI3T, with a total of 241 scans). Experimental results demonstrate that the proposed method outperforms the four common brain extraction methods (BET, BSE, GCUT, and ROBEX), and is comparable to the performance of BEaST, while being more accurate on some datasets compared with BEaST.
authors: School of Biomedical Engineering, Southern Medical University, Guangzhou, China. Electronic address: huangmeiyan11@gmail.com.
authors: Meiyan
authors: M
authors: Huang
authors: School of Biomedical Engineering, Southern Medical University, Guangzhou, China. Electronic address: weiyanggm@gmail.com.
authors: Wei
authors: W
authors: Yang
authors: School of Biomedical Engineering, Southern Medical University, Guangzhou, China. Electronic address: smujiang@gmail.com.
authors: Jun
authors: J
authors: Jiang
authors: School of Biomedical Engineering, Southern Medical University, Guangzhou, China. Electronic address: wuyao198851@gmail.com.
authors: Yao
authors: Y
authors: Wu
authors: School of Biomedical Engineering, Southern Medical University, Guangzhou, China. Electronic address: gz.akita.zy@gmail.com.
authors: Yu
authors: Y
authors: Zhang
authors: School of Biomedical Engineering, Southern Medical University, Guangzhou, China. Electronic address: wufanchen@gmail.com.
authors: Wufan
authors: W
authors: Chen
authors: School of Biomedical Engineering, Southern Medical University, Guangzhou, China. Electronic address: qianjinfeng08@gmail.com.
authors: Qianjin
authors: Q
authors: Feng
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000544:Alzheimer Disease / Q000473:pathology*; D001921:Brain / Q000473:pathology*; D002648:Child; D003198:Computer Simulation; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012886:Skull / Q000473:pathology; D013382:Subtraction Technique; D055815:Young Adult
keywords: Brain extraction; Label fusion; Local anchor embedding; Locally Linear Representation-based Classification