AI Article Synopsis
- The paper introduces a new active contours model designed to segment human leg bones in CT images using a variable combination of local and global intensity.
- This model is effective at separating objects with weak and strong boundaries and can distinguish closely situated structures.
- The authors developed an evolution equation for a level set function and used a histogram-based method for initial contour detection, demonstrating the model's effectiveness on CT scans from three patients.
Article Abstract
In this paper, we present a new active contours model to segment human leg bones in computed tomography images that is based on a variable-weighted combination of local and global intensity. This model can split an object surrounded by both weak and strong boundaries, and also distinguish very adjacent objects with those boundaries. The ability of this model is required for segmentation in medical images, e.g., human leg bones, which are usually composed of highly inhomogeneous objects and where the distances among organs are very close. We developed an evolution equation of a level set function whose zero level set represents a contour. An initial contour is automatically obtained by applying a histogram based multiphase segmentation method. We experimented with computed tomography images from three patients, and demonstrate the efficiency of the proposed method in experimental results.
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| http://dx.doi.org/10.1109/EMBC.2014.6944672 | DOI Listing |
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