Segmentation of MR image using local and global region based geodesic model.
Segmentation of MR image using local and global region based geodesic model.
复制标题
使用基于局部和全局区域的测地线模型分割 MR 图像
DOI：
10.1186/1475-925x-14-8
复制
发表时间：
2015-02-19
影响因子：
3.9
通讯作者：
Li W
中科院分区：
文献类型：
--
作者：
Li X;Jiang D;Shi Y;Li W
背景磁共振（MR）图像的分割是医学图像分析中的一个重要环节.由于未知噪声和弱边界导致的灰度不均匀性使分割成为一个难题，本文提出了一种新的水平集测地线模型，该模型将局部和全局灰度信息融合在有符号压力（SPF）函数中，以抑制灰度不均匀性并实现分割。首先，提出了一种新的基于局部和全局区域的SPF函数来提取局部和全局图像信息，以确保对象轮廓的灵活初始化。其次，全局SPF是自适应平衡的权重计算通过使用局部图像对比度。第三，两个阶段的水平集配方扩展到一个多阶段的配方，成功地分割脑MR images.ResultsThe合成图像和MR图像的实验结果表明，该方法是非常强大和有效的。与相关的方法相比，我们的方法是更有效的计算和更不敏感的初始轮廓。此外，18 T1加权脑MR图像（国际脑分割库）上的验证表明，我们的方法可以产生非常有前途的results.ConclusionsA新的分割模型，将局部和全局信息到原来的GAC模型。该模型适用于非均匀MR图像的分割，并允许灵活的初始化。
BackgroundSegmentation of the magnetic resonance (MR) images is fundamentally important in medical image analysis. Intensity inhomogeneity due to the unknown noise and weak boundary makes it a difficult problem.MethodThe paper presents a novel level set geodesic model which integrates the local and the global intensity information in the signed pressure force (SPF) function to suppress the intensity inhomogeneity and implement the segmentation. First, a new local and global region based SPF function is proposed to extract the local and global image information in order to ensure a flexible initialization of the object contours. Second, the global SPF is adaptively balanced by the weight calculated by using the local image contrast. Third, two-phase level set formulation is extended to a multi-phase formulation to successfully segment brain MR images.ResultsExperimental results on the synthetic images and MR images demonstrate that the proposed method is very robust and efficient. Compared with the related methods, our method is much more computationally efficient and much less sensitive to the initial contour. Furthermore, the validation on 18 T1-weighted brain MR images (International Brain Segmentation Repository) shows that our method can produce very promising results.ConclusionsA novel segmentation model by incorporating the local and global information into the original GAC model is proposed. The proposed model is suitable for the segmentation of the inhomogeneous MR images and allows flexible initialization.