title: Automatic segmentation of liver structure in CT images.
abstract: The segmentation and three-dimensional representation of the liver from a computed tomography (CT) scan is an important step in many medical applications, such as in the surgical planning for a living-donor liver transplant and in the automatic detection and documentation of pathological states. A method is being developed to automatically extract liver structure from abdominal CT scans using a priori information about liver morphology and digital image-processing techniques. Segmentation is performed sequentially image-by-image (slice-by-slice), starting with a reference image in which the liver occupies almost the entire right half of the abdomen cross section. Image processing techniques include gray-level thresholding, Gaussian smoothing, and eight-point connectivity tracking. For each case, the shape, size, and pixel density distribution of the liver are recorded for each CT image and used in the processing of other CT images. Extracted boundaries of the liver are smoothed using mathematical morphology techniques and B-splines. Computer-determined boundaries were compared with those drawn by a radiologist. The boundary descriptions from the two methods were in agreement, and the calculated areas were within 10%.
authors.affiliation: Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, University of Chicago, Illinois 60637.
authors.forename: K T
authors.initials: KT
authors.lastname: Bae
authors.forename: M L
authors.initials: ML
authors.lastname: Giger
authors.forename: C T
authors.initials: CT
authors.lastname: Chen
authors.forename: C E
authors.initials: CE
authors.lastname: Kahn
mesh_terms: D055592:Biophysical Phenomena; D001703:Biophysics; D005069:Evaluation Studies as Topic; D006801:Humans; D008099:Liver / Q000000981:diagnostic imaging*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D011860:Radiography, Abdominal; D014057:Tomography, X-Ray Computed / Q000706:statistics & numerical data*