An Improved Level Set for Liver Segmentation and Perfusion Analysis in MRIs
An Improved Level Set for Liver Segmentation and Perfusion Analysis in MRIs
复制标题
MRI 中肝脏分割和灌注分析的改进水平集
DOI：
10.1109/titb.2008.2007110
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发表时间：
2009
影响因子：
--
通讯作者：
Gu, Lixu
中科院分区：
文献类型：
--
作者：
Qian, Lijun;Xu, Jianrong;Chen, Gang;Gu, Lixu
从磁共振成像中准确地确定肝脏分割是任何自动化肝脏灌注分析的首要和关键步骤，它提供了关于肝脏血液供应的重要信息。虽然水平集方法和活动轮廓等隐式轮廓提取方法常用于肝脏分割，但由于肝脏边界存在伪影和低梯度响应，分割结果并不总是令人满意。在本文中，我们提出了一种多初始化、多步骤的最小二乘法来克服泄漏和过分割问题。首先使用快速进行法和最小二乘法分别对多个初始化曲线进行进化，然后结合凸壳算法得到粗略的肝脏轮廓。最后，使用全局水平集平滑来确定精确的肝脏边界，从而再次进化轮廓。在12个腹部MRI序列上的实验结果表明，该方法获得了较好的肝脏分割结果，利用改进的倒角匹配算法可以得到不受呼吸影响的精细肝脏灌注曲线，并由放射科医生对其进行评估。
Determining liver segmentation accurately from MRIs is the primary and crucial step for any automated liver perfusion analysis, which provides important information about the blood supply to the liver. Although implicit contour extraction methods, such as level set methods (LSMs) and active contours, are often used to segment livers, the results are not always satisfactory due to the presence of artifacts and low-gradient response on the liver boundary. In this paper, we propose a multiple-initialization, multiple-step LSM to overcome the leakage and over-segmentation problems. The multiple-initialization curves are first evolved separately using the fast marching methods and LSMs, which are then combined with a convex hull algorithm to obtain a rough liver contour. Finally, the contour is evolved again using global level set smoothing to determine a precise liver boundary. Experimental results on 12 abdominal MRI series showed that the proposed approach obtained better liver segmentation results, so that a refined liver perfusion curve without respiration affection can be obtained by using a modified chamfer matching algorithm and the perfusion curve is evaluated by radiologists.
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DOI：
10.1007/11866763_6
发表时间：
2006-10
期刊：
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
影响因子：
--
作者：
T. Heimann;I. Wolf;H. Meinzer
通讯作者：
T. Heimann;I. Wolf;H. Meinzer
影响因子：
6
作者：
Materne, R;Van Beers, BE;Horsmans, Y
通讯作者：
Horsmans, Y
DOI：
10.1117/12.710422
发表时间：
2007-03
期刊：
--
影响因子：
--
作者：
M. Pham;Ruchaneewan Susomboon;Tim Disney;D. Raicu;Jacob Furst
通讯作者：
M. Pham;Ruchaneewan Susomboon;Tim Disney;D. Raicu;Jacob Furst
影响因子：
10.6
作者：
Chan, TF;Vese, LA
通讯作者：
Vese, LA
DOI：
10.1007/978-3-540-75757-3_11
发表时间：
2007-10
期刊：
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
影响因子：
--
作者：
T. Okada;Ryuji Shimada;Yoshinobu Sato;M. Hori;Keita Yokota;M. Nakamoto;Yenwei Chen;Hironobu Nakamura;S. Tamura
通讯作者：
T. Okada;Ryuji Shimada;Yoshinobu Sato;M. Hori;Keita Yokota;M. Nakamoto;Yenwei Chen;Hironobu Nakamura;S. Tamura