Total Variation Constrained Non-Negative Matrix Factorization for Medical Image Registration

• 来源期刊: 自动化学报（英文版）
• 年，卷（期）: 2021,（5）
• 所属期刊栏目: PAPERS
• 页码范围: 1025-1037
• 页数: 13页
• 语种: 英文
• DOI: 10.1109/JAS.2021.1003979

摘要

This paper presents a novel medical image registration algorithm named total variation constrained graph-regularization for non-negative matrix factorization (TV-GNMF). The method utilizes non-negative matrix factorization by total variation constraint and graph regularization. The main contributions of our work are the following. First, total variation is incorporated into NMF to control the diffusion speed. The purpose is to denoise in smooth regions and preserve features or details of the data in edge regions by using a diffusion coefficient based on gradient information. Second, we add graph regularization into NMF to reveal intrinsic geometry and structure information of features to enhance the discrimination power. Third, the multiplicative update rules and proof of convergence of the TV-GNMF algorithm are given. Experiments conducted on datasets show that the proposed TV-GNMF method outperforms other state-of-the-art algorithms.