An Improved Matrix Factorization Based Active Contours Combining Edge Preservation for Image Segmentation

Image segmentation is a crucial role towards clinical diagnosis and therapy planning due to the existence of abundant noise, blurry boundaries and heterogeneity. In this work, a novel matrix factorization based approach with the ability of edge preservation is presented. Firstly, to obtain more comprehensive feature description, we use the local spectral histograms to describe the local structures formed by feature values. Secondly, the energy function is established via matrix factorization theory, which makes each pixel fall into the sub-region with the largest coverage area in its neighborhood. Then, the edge preservation is used to obtain a smoother and more accurate object boundary. Finally, a number of synthetic and natural images are performed for verification. Experiments demonstrated that our approach achieves satisfactory results and has more robust against the complex background than other methods.