title: Segmentation of images of abdominal organs.
abstract: Abdominal organ segmentation, which is, the delineation of organ areas in the abdomen, plays an important role in the process of radiological evaluation. Attempts to automate segmentation of abdominal organs will aid radiologists who are required to view thousands of images daily. This review outlines the current state-of-the-art semi-automated and automated methods used to segment abdominal organ regions from computed tomography (CT), magnetic resonance imaging (MEI), and ultrasound images. Segmentation methods generally fall into three categories: pixel based, region based and boundary tracing. While pixel-based methods classify each individual pixel, region-based methods identify regions with similar properties. Boundary tracing is accomplished by a model of the image boundary. This paper evaluates the effectiveness of the above algorithms with an emphasis on their advantages and disadvantages for abdominal organ segmentation. Several evaluation metrics that compare machine-based segmentation with that of an expert (radiologist) are identified and examined. Finally, features based on intensity as well as the texture of a small region around a pixel are explored. This review concludes with a discussion of possible future trends for abdominal organ segmentation.
authors.affiliation: Department of Computing and Software, School of Biomedical Engineering, McMaster University, Hamilton, Ontario, Canada.
authors.forename: Jie
authors.initials: J
authors.lastname: Wu
authors.forename: Markad V
authors.initials: MV
authors.lastname: Kamath
authors.forename: Michael D
authors.initials: MD
authors.lastname: Noseworthy
authors.forename: Colm
authors.initials: C
authors.lastname: Boylan
authors.forename: Skip
authors.initials: S
authors.lastname: Poehlman
mesh_terms: D000005:Abdomen / Q000033:anatomy & histology*; D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003952:Diagnostic Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014781:Viscera / Q000033:anatomy & histology*