Hong Qingqi, Li Qingde, Wang Beizhan, Li Yan, Yao Junfeng, Liu Kunhong, Wu Qingqiang
Software School, Xiamen University, 361005 Xiamen, China.
Biomed Eng Online. 2014 Dec 16;13:169. doi: 10.1186/1475-925X-13-169.
Intensity inhomogeneity occurs in many medical images, especially in vessel images. Overcoming the difficulty due to image inhomogeneity is crucial for the segmentation of vessel image.
This paper proposes a localized hybrid level-set method for the segmentation of 3D vessel image. The proposed method integrates both local region information and boundary information for vessel segmentation, which is essential for the accurate extraction of tiny vessel structures. The local intensity information is firstly embedded into a region-based contour model, and then incorporated into the level-set formulation of the geodesic active contour model. Compared with the preset global threshold based method, the use of automatically calculated local thresholds enables the extraction of the local image information, which is essential for the segmentation of vessel images.
Experiments carried out on the segmentation of 3D vessel images demonstrate the strengths of using locally specified dynamic thresholds in our level-set method. Furthermore, both qualitative comparison and quantitative validations have been performed to evaluate the effectiveness of our proposed model.
Experimental results and validations demonstrate that our proposed model can achieve more promising segmentation results than the original hybrid method does.
强度不均匀性在许多医学图像中都会出现，尤其是在血管图像中。克服图像不均匀性带来的困难对于血管图像分割至关重要。
本文提出了一种用于三维血管图像分割的局部混合水平集方法。该方法将局部区域信息和边界信息整合用于血管分割，这对于精确提取微小血管结构至关重要。首先将局部强度信息嵌入基于区域的轮廓模型，然后纳入测地线活动轮廓模型的水平集公式中。与基于预设全局阈值的方法相比，使用自动计算的局部阈值能够提取局部图像信息，这对于血管图像分割至关重要。
在三维血管图像分割上进行的实验证明了在我们的水平集方法中使用局部指定动态阈值的优势。此外，还进行了定性比较和定量验证以评估我们提出模型的有效性。
实验结果和验证表明，我们提出的模型比原始混合方法能取得更有前景的分割结果。
