一种各向异性Wells算法脑核磁共振图像分割模型

核磁共振图像分析已经成为主要的医疗辅助手段之一.然而,由于偏移场的影响导致该类图像的分析较为困难,去偏移场已成为图像分析的必要步骤.Wells算法将图像分割和去偏移场放入同一个框架内并取得较好的结果.然而该算法没有考虑像素间的位置信息,因而导致该算法对图像噪声比较敏感.为了克服其局限性,利用Gibbs理论和图像结构信息构造各向异性Gibbs随机场,并将其引入到Wells算法的框架中,完善其分类效果,使其克服噪声影响的同时还能够保持细长拓扑结构区域信息以及角点区域信息.实验证明提出的算法可以得到较好的分类结果.

Brain MR Image Segmentation Based on Anisotropic Wells Model

Nuclear magnetic resonance (MR) image analysis has become a major means of the auxiliary medical services. However, intensity inhomogeneity, which is usually named as bias field, causes considerable difficulty in the quantitative analysis of MR images. Thus bias field estimation is a necessary pre-processing step before quantitative analysis of MR data. The Wells model, one of the widely used methods, uses knowledge of tissue intensity properties and intensity inhomogeneities to correct and segment MR images. However, the classical Wells model only uses the intensity information and no spatial information is taken into account, so it is sensitive to the noise. In order to overcome this limitation, the Gibbs theory and the image structure information are used to construct anisotropic Gibbs random field. The traditional Gibbs theory usually loses the information of the beam structure regions and the corner regions. With the spatial information, the anisotropic Gibbs random field can reduce the effect of the noise and contain the information of the beam structure regions and the corner regions. The anisotropic Gibbs random field is incorporated into the Wells model. The experiments of segmenting the brain magnetic resonance images show that the proposed method can obtain better results in an accurate way.