多尺度非参数化水平集的超声心动图分割

高燕华 1; 刘玉欢 2; 喻罡 2

doi:10.7652/xjtuxb201302009

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多尺度非参数化水平集的超声心动图分割

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- 多尺度非参数化水平集的超声心动图分割
- Multiscale Non-Parametric Level Set Segmentation of Ultrasound Echocardiography
- 西安交通大学学报 2013年47卷第2期页码：53-57+96
- 作者机构：
  
  西安交通大学第三附属医院,西安,710068
- 作者简介：
- 基金信息：
  
  : 2012-05-16。作者简介: 高燕华(1977—),女,硕士,主治医师;喻罡(通信作者),男,博士,副教授。基金项目: 国家自然科学基金资助项目(81000635)
- DOI：10.7652/xjtuxb201302009
  中图分类号： TN911.7
- 网络首发：2013-02-10，
  
  纸质出版：2013
- 稿件说明：
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高燕华 1, 刘玉欢 2, 喻罡 2. 多尺度非参数化水平集的超声心动图分割[J]. 西安交通大学学报, 2013,47(2):53-57+96.

Multiscale Non-Parametric Level Set Segmentation of Ultrasound Echocardiography[J]. 2013, 47(2): 53-57+96.
高燕华 1, 刘玉欢 2, 喻罡 2. 多尺度非参数化水平集的超声心动图分割[J]. 西安交通大学学报, 2013,47(2):53-57+96. DOI： 10.7652/xjtuxb201302009.

Multiscale Non-Parametric Level Set Segmentation of Ultrasound Echocardiography[J]. 2013, 47(2): 53-57+96. DOI： 10.7652/xjtuxb201302009.

摘要

针对超声心动图噪声很大、提取目标区域边界不够平滑完整的问题

将非参数技术与水平集相结合

提出了多尺度非参数化的水平集图像分割方法。利用非局域均值滤波建立尺度空间

保护图像特征

在粗尺度预分割

然后在细尺度优化分割。采用Parzen窗技术对超声心动图的亮度分布进行统计建模

不需要先验假设

引入到水平集框架中

设计了非参数化水平集分割模型。分割实验证明:预分割结果和真实边界的平均绝对距离为2.162

优化后为0.710。该方法可以精确地自动提取感兴趣区域

在图像分割鲁棒性和精确性方面优于常规分割方法。

Abstract

To solve difficulty that the boundary of segmented objective region is not enough smooth and complete due to ultrasound echocardiography with serious noise

a multiscale segmentation approach combined non-parametric technique with level set

is presented. The nonlocal means filtering(NLM)is performed to create scale space and preserve image features. Pre-segmentation is firstly carried out in a coarser scale image

then an optimized segmentation in a finer scale image

the intensity distribution of the ultrasound echo images is modeled by Parzen window technique without prior assumption. A non-parametric model on level set framework is designed to segment the ultrasound echocardiography. The segmentation experiments show that the mean absolute distance(MAD)between real boundary and pre-segmentation result gets to 2.162

but

and the optimized result only 0.710. The approach outperforms the conventional segmentation methods by accurately and automatically extracting the regions of interest.

关键词

Keywords

references

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MALLADI R, SETHIAN J A, VEMURI B C. Shape modeling with front propagation: a level set approach [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995, 17(2): 158-175.

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