Paper Title:
Edge Based Level Set with Gaussian Filtering Regularized and its Application in Liver Segmentation
  Abstract

A novel edge-based active contour model (ACM) is proposed in this paper. Our edge-based active contour model has many advantages over the conventional active contour models. Firstly, the proposed model can get much smoother contour and needs much less iterations to evolution by being implemented with a special processing named Selectively Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) method. Secondly, we introduce Bilateral Gaussian Filter which can preserve edges to smooth images. So we make weak edges more clear than traditional Gaussian Filter. Thirdly, the level set function can be easily initialized with binary function, which is more efficient to construct than the widely used signed distance function (SDF) because of the special processing. Experiments on synthetic image and segmenting liver from abdominal CT images demonstrate the advantages of the proposed method over geodesic active contours (GAC) in term of both efficiency and accuracy.

  Info
Periodical
Advanced Materials Research (Volumes 121-122)
Edited by
Donald C. Wunsch II, Honghua Tan, Dehuai Zeng, Qi Luo
Pages
222-227
DOI
10.4028/www.scientific.net/AMR.121-122.222
Citation
R. J. Feng, H. Y. Jiang, "Edge Based Level Set with Gaussian Filtering Regularized and its Application in Liver Segmentation", Advanced Materials Research, Vols. 121-122, pp. 222-227, 2010
Online since
June 2010
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Price
$32.00
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