Medical Research Center, Seoul National University, Daehak-ro, Jongno-gu, Seoul, South Korea.
Comput Biol Med. 2012 May;42(5):523-37. doi: 10.1016/j.compbiomed.2012.01.005. Epub 2012 Mar 7.
In this paper, we present a new segmentation method using the level set framework for medical volume images. The method was implemented using the surface evolution principle based on the geometric deformable model and the level set theory. And, the speed function in the level set approach consists of a hybrid combination of three integral measures derived from the calculus of variation principle. The terms are defined as robust alignment, active region, and smoothing. These terms can help to obtain the precise surface of the target object and prevent the boundary leakage problem. The proposed method has been tested on synthetic and various medical volume images with normal tissue and tumor regions in order to evaluate its performance on visual and quantitative data. The quantitative validation of the proposed segmentation is shown with higher Jaccard's measure score (72.52%-94.17%) and lower Hausdorff distance (1.2654 mm-3.1527 mm) than the other methods such as mean speed (67.67%-93.36% and 1.3361mm-3.4463 mm), mean-variance speed (63.44%-94.72% and 1.3361 mm-3.4616 mm), and edge-based speed (0.76%-42.44% and 3.8010 mm-6.5389 mm). The experimental results confirm that the effectiveness and performance of our method is excellent compared with traditional approaches.
本文提出了一种新的基于水平集框架的医学体数据集分割方法。该方法采用基于几何变形模型和水平集理论的曲面演化原理实现。并且，水平集方法中的速度函数由三个来自变分原理的积分度量的混合组合构成。这些项定义为稳健对齐、活动区域和平滑。这些项有助于获得目标对象的精确表面，并防止边界泄漏问题。为了评估该方法在视觉和定量数据上的性能，已经在合成和具有正常组织和肿瘤区域的各种医学体数据集上对该方法进行了测试。与其他方法（如平均速度（67.67％-93.36％和 1.3361mm-3.4463mm）、均值-方差速度（63.44％-94.72％和 1.3361mm-3.4616mm）和基于边缘的速度（0.76％-42.44％和 3.8010mm-6.5389mm）相比，该方法的 Jaccard 度量得分（72.52％-94.17％）和 Hausdorff 距离（1.2654mm-3.1527mm）都更高。实验结果证实，与传统方法相比，我们的方法在有效性和性能方面都非常出色。
