Segmentation of kidney from ultrasound B-mode images with texture-based classification

Chia Hsiang Wu, Yung Nien Sun

研究成果: Article同行評審

29 引文 斯高帕斯(Scopus)


The segmentation of anatomical structures from sonograms can help physicians evaluate organ morphology and realize quantitative measurement. It is an important but difficult issue in medical image analysis. In this paper, we propose a new method based on Laws' microtexture energies and maximum a posteriori (MAP) estimation to construct a probabilistic deformable model for kidney segmentation. First, using texture image features and MAP estimation, we classify each image pixel as inside or outside the boundary. Then, we design a deformable model to locate the actual boundary and maintain the smooth nature of the organ. Using gradient information subject to a smoothness constraint, the optimal contour is obtained by the dynamic programming technique. Experiments on different datasets are described. We find this method to be an effective approach.

頁(從 - 到)114-123
期刊Computer Methods and Programs in Biomedicine
出版狀態Published - 2006 十二月

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Health Informatics

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