集成非线性统计形状先验的M-S图像分割模型

M-S Model with Nonlinear Statistical Shape Prior for Image Segmentation

为克服噪声污染、血管遮挡、光照不均匀、对比度小、个体间差异大等视网膜和视神经细微组织结构医学图像分割中固有的困难,提出了一种集成非线性形状先验的医学图像分割新方法.该方法首先采用非线性的核函数将目标先验形状窄带水平集映射到其核空间,然后在核空间进行主成分分析(PCA),以获取目标形状窄带水平集核空间的基底向量,并据此将目标形状先验知识集成到Mumford-Shah向量值图像分割模型,实现医学图像的分割.不同青光眼病人的视乳头图像分割实验结果表明,该方法能够有效地分割噪声大、对比度小且部分被血管遮挡的各阶段的青光眼病人视乳头图像.

A modified Mumford-Shah model with nonlinear statistical shape prior was proposed to segment the optic nerve head in fundus images of poor quality, very low contrast, obscurity due to blood vessels, and distinct inter-differences of individuals. Firstly, the narrow band level set of shape prior was mapped into its kernel space by a nonlinear kernel function. Then, principal component analysis （PCA） was performed in the kernel space so as to acquire its base vectors, and statistical shape prior was integrat- ed into a Mumford-Shah model. The segmentation results of the color optic nerve head images of patients in different stages of glaucoma have showed that the proposed model is effective and practicable.