Knowledge Based Hierarchical Mumford-Shah Model for Vector-valued Image Segmentation
基于知识的多层Mumford-Shah向量值图像分割模型

To segment a given vector-valued image such as color images, and to handle important image features such as very low contrast and obscured part, a novel knowledge based hierarchical Mumford-Shah functional model is addressed by integrating a statistical shape prior model based on narrow band level set with hierarchical Mumford-Shah model. At the same time, an iterative tier-by-tier algorithm based on techniques of level set is proposed to minimize the new functional. This novel model could recognise an object whose shape is similar to the prior one. The experimental results show that the technique is effective and practicable by applying it to the segmentation of the optic disk obscured by blood vessels in color optic nerve head images of early stage glaucoma patients.