title: Liver segmentation using location and intensity probabilistic atlases.
abstract: In a variety of injuries and illnesses, internal organs in the abdominal and pelvic regions, in particular liver, may be compromised. In the current practice, CT scans of liver are visually inspected to investigate the integrity of the organ. However, the size and complexity of the CT images limits the reliability of visual inspection to accurately assess the health of liver. Computer-aided image analysis can create fast and quantitative assessment of liver from the CT, in particular in the environments where access to skilled radiologists may be limited. In this paper we propose a hierarchical method based on probabilistic models of position and intensity of voxels for automated segmentation of liver that achieves the Dice similarity coefficient of higher than 89%.
authors.forename: Negar
authors.initials: N
authors.lastname: Farzaneh
authors.forename: Shadrokh
authors.initials: S
authors.lastname: Samavi
authors.forename: S M
authors.initials: SM
authors.lastname: Reza Soroushmehr
authors.forename: Hirenkumar
authors.initials: H
authors.lastname: Patel
authors.forename: Samuel
authors.initials: S
authors.lastname: Habbo-Gavin
authors.forename: David Paul
authors.initials: DP
authors.lastname: Fessell
authors.forename: Kevin R
authors.initials: KR
authors.lastname: Ward
authors.forename: Kayvan
authors.initials: K
authors.lastname: Najarian
mesh_terms: D000465:Algorithms; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008099:Liver / Q000000981:diagnostic imaging*; D008297:Male; D015233:Models, Statistical; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed