title: Medical image segmentation using analysis of isolable-contour maps.
abstract: A common challenge for automated segmentation techniques is differentiation between images of close objects that have similar intensities, whose boundaries are often blurred due to partial-volume effects. We propose a novel approach to segmentation of two-dimensional images, which addresses this challenge. Our method, which we call intrinsic shape for segmentation (ISeg), analyzes isolabel-contour maps to identify coherent regions that correspond to major objects. ISeg generates an isolabel-contour map for an image by multilevel thresholding with a fine partition of the intensity range. ISeg detects object boundaries by comparing the shape of neighboring isolabel contours from the map. ISeg requires only little effort from users; it does not require construction of shape models of target objects. In a formal validation with computed-tomography angiography data, we showed that ISeg was more robust than conventional thresholding, and that ISeg's results were comparable to results of manual tracing.
authors.affiliation: Department of Psychiatry, Stanford University, CA 94305, USA.
authors.forename: S
authors.initials: S
authors.lastname: Shiffman
authors.forename: G D
authors.initials: GD
authors.lastname: Rubin
authors.forename: S
authors.initials: S
authors.lastname: Napel
mesh_terms: D000465:Algorithms; D000792:Angiography / Q000379:methods*; D006801:Humans; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed