2019/04/24 by Dong Wang, Xiaoping Wang, Wang, Dong +1 · 3 citations
Computer Science · Medicine · #Medical Image Segmentation Techniques #Radiomics and Machine Learning in Medical Imaging #Image and Signal Denoising Methods
paper · pdf · doi:10.48550/arxiv.1904.10917
In this paper, we propose a novel iterative convolution-thresholding method (ICTM) that is applicable to a range of variational models for image segmentation. A variational model usually minimizes an energy functional consisting of a fidelity term and a regularization term. In the ICTM, the interface between two different segment domains is implicitly represented by their characteristic functions. The fidelity term is then usually written as a linear functional of the characteristic functions and the regularized term is approximated by a functional of characteristic functions in terms of heat kernel convolution. This allows us to design an iterative convolution-thresholding method to minimize the approximate energy. The method is simple, efficient and enjoys the energy-decaying property. Numerical experiments show that the method is easy to implement, robust and applicable to various image segmentation models.