基于图割和边缘行进的肝脏CT序列图像分割

廖苗; 赵于前; 曾业战; 黄忠朝; 邹北骥

doi:10.11999/JEIT151005

基于图割和边缘行进的肝脏CT序列图像分割

doi: 10.11999/JEIT151005

1.
(中南大学地球科学与信息物理学院长沙 410083) ②(中南大学信息科学与工程学院长沙 410083)

基金项目:

国家自然科学基金(61172184, 61379107, 61402539, 61174210)，新世纪优秀人才支持计划(NCET-13-0603)，高等学校博士学科点专项科研基金(20130162110016)，湖南省科技基本建设项目(20131199)，湖南省科技计划项目(2015RS4008)，中南大学中央高校基本科研业务费专项资金(2014ZZTS053)，湖南省研究生科研创新项目(CX2014B052)

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出版历程
- 收稿日期: 2015-09-08
- 修回日期: 2016-01-22
- 刊出日期: 2016-06-19

Liver Segmentation from Abdominal CT Volumes Based on Graph Cuts and Border Marching

1.
(School of Geosciences and Info-Physics, Central South University, Changsha 410083, China)

Funds:

The National Natural Science Foundation of China (61172184, 61379107, 61402539, 61174210), Program for New Century Excellent Talents in University of Ministry of Education in China (NCET-13-0603), Specialized Research Fund for the Doctoral Program of Higher Education in China (20130162110016), Program for Hunan Province Science and Technology Basic Construction (Grant 20131199), Hunan Provincial Science and Technology Project of China (2015RS4008), Fundamental Research Funds for the Central Universities of Central South University (2014ZZTS053), Hunan Provincial Innovation Foundation for Postgraduate (CX2014B052)

摘要

摘要: 提出一种新的基于图割和边缘行进的腹部CT序列图像肝脏分割方法。首先，针对输入序列的数据特征，建立肝脏亮度和外观模型，突出肝脏区域抑制非肝脏区域；然后，将肝脏亮度、外观模型以及相邻切片之间的位置信息有效融入图割能量函数，实现CT序列肝脏的自动初步分割；最后，针对血管欠分割问题，提出了一种基于边缘行进的结果优化方法。通过对XHCSU14和SLIVER07数据库提供的30个病人肝脏序列的分割实验，以及与其他多种肝脏分割方法的比较，表明该方法能完整有效地分割肝脏，准确性高，鲁棒性强。
- 医学图像分割 /
- 图割 /
- 边缘行进 /
- 高斯拟合 /
- 主成分分析
Abstract: A novel method for liver segmentation from abdominal CT volumes based on graph cuts and border marching is proposed. First, to exclude complex background and highlight liver region, liver intensity and appearance models are built according to the characteristics of a given CT volume. Then, the intensity and appearance models together with location information from neighbor segmented slice are effectively integrated into graph cuts cost computation to segment the CT volume initially and automatically. Finally, to solve the under-segmentation issue of liver vessel, a boundary compensation method based on border marching is proposed. The proposed method is tested and compared with some other methods on 30 CT volumes from XHCSU14 and SLIVER07 databases. The experimental results show that the proposed method can segment livers integrally and effectively from abdominal CT volumes, with higher accuracy and robustness.
- Medical image segmentation /
- Graph cuts /
- Border marching /
- Gaussian fitting /
- Principal Components Analysis (PCA)

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参考文献(13)

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