title: A knowledge-driven algorithm for a rapid and automatic extraction of the human cerebral ventricular system from MR neuroimages.
abstract: A knowledge-driven algorithm for a rapid, robust, accurate, and automatic extraction of the human cerebral ventricular system from MR neuroimages is proposed. Its novelty is in combination of neuroanatomy, radiological properties, and variability of the ventricular system with image processing techniques. The ventricular system is divided into six 3D regions: bodies and inferior horns of the lateral ventricles, third ventricle, and fourth ventricle. Within each ventricular region, a 2D region of interest (ROI) is defined based on anatomy and variability. Each ventricular region is further subdivided into subregions, and conditions detecting and preventing leakage into the extra-ventricular space are specified for each subregion. The algorithm extracts the ventricular system by (1) processing each ROI (to calculate its local statistics, determine local intensity ranges of cerebrospinal fluid and gray and white matters, set a seed point within the ROI, grow region directionally in 3D, check anti-leakage conditions, and correct growing if leakage occurred) and (2) connecting all unconnected regions grown by relaxing growing conditions. The algorithm was validated qualitatively on 68 and quantitatively on 38 MRI normal and pathological cases (30 clinical, 20 MGH Brain Repository, and 18 MNI BrainWeb data sets). It runs successfully for normal and pathological cases provided that the slice thickness is less than 3.0 mm in axial and less than 2.0 mm in coronal directions, and the data do not have a high inter-slice intensity variability. The algorithm also works satisfactorily in the presence of up to 9% noise and up to 40% RF inhomogeneity for the BrainWeb data. The running time is less than 5 s on a Pentium 4, 2.0 GHz PC. The best overlap metric between the results of a radiology expert and the algorithm is 0.9879 and the worst 0.9527; the mean and standard deviation of the overlap metric are 0.9723 and 0.01087, respectively.
authors.affiliation: Biomedical Imaging Laboratory, Institute for Infocomm Research, 119613, Singapore, Singapore. yanxia@i2r.a-star.edu.sg
authors.forename: Yan
authors.initials: Y
authors.lastname: Xia
authors.forename: Qingmao
authors.initials: Q
authors.lastname: Hu
authors.forename: Aamer
authors.initials: A
authors.lastname: Aziz
authors.forename: Wieslaw L
authors.initials: WL
authors.lastname: Nowinski
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology*; D001932:Brain Neoplasms / Q000175:diagnosis; D002552:Cerebral Ventricles / Q000473:pathology*; D002648:Child; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D006849:Hydrocephalus / Q000175:diagnosis; D007089:Image Enhancement / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008432:Mathematical Computing; D019047:Phantoms, Imaging; D012016:Reference Values; D012680:Sensitivity and Specificity; D012984:Software