Medical image segmentation using analysis of isolable-contour maps.
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.
Duke Scholars
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Related Subject Headings
- Tomography, X-Ray Computed
- Sensitivity and Specificity
- Nuclear Medicine & Medical Imaging
- Humans
- Angiography
- Algorithms
- 46 Information and computing sciences
- 40 Engineering
- 09 Engineering
- 08 Information and Computing Sciences
Citation
Published In
DOI
ISSN
Publication Date
Volume
Issue
Start / End Page
Location
Related Subject Headings
- Tomography, X-Ray Computed
- Sensitivity and Specificity
- Nuclear Medicine & Medical Imaging
- Humans
- Angiography
- Algorithms
- 46 Information and computing sciences
- 40 Engineering
- 09 Engineering
- 08 Information and Computing Sciences