Medical image segmentation using analysis of isolable-contour maps

Smadar Shiftman, Geoffrey D. Rubin, Sandy Napel

Research output: Contribution to journalArticlepeer-review

44 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1064-1074
Number of pages11
JournalIEEE Transactions on Medical Imaging
Issue number11
StatePublished - Nov 2000
Externally publishedYes

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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