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

Chia Hsiang Wu, Yung-Nien Sun

研究成果: Article

26 引文 (Scopus)

摘要

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.

原文English
頁(從 - 到)114-123
頁數10
期刊Computer Methods and Programs in Biomedicine
84
發行號2-3
DOIs
出版狀態Published - 2006 十二月 1

指紋

Textures
Ultrasonics
Kidney
Statistical Models
Dynamic programming
Image analysis
Pixels
Physicians
Experiments
Datasets

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Health Informatics

引用此文

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