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.

中文翻译：

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医学图像配准的总变分约束非负矩阵分解


本文提出了一种新颖的医学图像配准算法，称为非负矩阵分解的全变分约束图正则化（TV-GNMF）。该方法利用全变分约束的非负矩阵分解和图正则化。我们工作的主要贡献如下。首先，将总变分纳入 NMF 中以控制扩散速度。目的是通过使用基于梯度信息的扩散系数，对平滑区域进行去噪，并保留边缘区域数据的特征或细节。其次，我们将图正则化添加到 NMF 中，以揭示特征的内在几何和结构信息，以增强区分能力。第三，给出了TV-GNMF算法的乘性更新规则和收敛性证明。对数据集进行的实验表明，所提出的 TV-GNMF 方法优于其他最先进的算法。