Li Xiuming, Jiang Dongsheng, Shi Yonghong, Li Wensheng
Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, PR China.
Department of Anatomy, Histology and Embryology, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, PR China.
Biomed Eng Online. 2015 Feb 19;14:8. doi: 10.1186/1475-925X-14-8.
Segmentation 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.
The 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.
Experimental 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.
A 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.
磁共振（MR）图像分割在医学图像分析中至关重要。由于未知噪声和弱边界导致的强度不均匀性使其成为一个难题。
本文提出了一种新颖的水平集测地线模型，该模型在符号压力力（SPF）函数中整合了局部和全局强度信息，以抑制强度不均匀性并实现分割。首先，提出了一种基于局部和全局区域的新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，通过使用局部图像对比度计算的权重对全局SPF进行自适应平衡。第三，将两相水平集公式扩展为多相公式，以成功分割脑部MR图像。
在合成图像和MR图像上的实验结果表明，所提出的方法非常稳健且高效。与相关方法相比，我们的方法计算效率更高，对初始轮廓的敏感度更低。此外，对18张T1加权脑部MR图像（国际脑部分割存储库）的验证表明，我们的方法可以产生非常有前景的结果。
提出了一种通过将局部和全局信息纳入原始GAC模型的新颖分割模型。所提出的模型适用于不均匀MR图像的分割，并允许灵活初始化。
