title: Segmentation of brain parenchyma using bilateral filtering and region growing.
abstract: When the non-diffusion weighted images (non-DWIs) and the diffusion weighted images (DWIs) are acquired by a fast imaging sequence, they suffer from several artifacts such as N/2 ghost, subject motion, eddy current, etc. These artifacts act as a noise in the background area of the human brain. To extract the brain region from the noisy background, brain parenchyma segmentation has been used. Several segmentation methods presented so far cannot address this problem well. In this study, we propose a novel segmentation method of brain contour in non-DWIs using bilateral filtering, which can reduce the background noise while edge-preserving, and region growing. We compare the segmentation results from various methods, and the proposed method shows better segmentation results than those from other schemes.
authors.affiliation: Division of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 305-701, Republic of Korea. jyhwang@athena.kaist.ac.kr
authors.forename: Jinyoung
authors.initials: J
authors.lastname: Hwang
authors.forename: Yeji
authors.initials: Y
authors.lastname: Han
authors.forename: HyunWook
authors.initials: H
authors.lastname: Park
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted