Specular Reflections Removal for Endoscopic Image Sequences With Adaptive-RPCA Decomposition
Specular Reflections Removal for Endoscopic Image Sequences With Adaptive-RPCA Decomposition
复制标题
利用自适应 rpca 分解去除内窥镜图像序列的镜面反射
DOI：
10.1109/tmi.2019.2926501
复制
发表时间：
2020-02-01
影响因子：
10.6
通讯作者：
Qin, Hong
中科院分区：
文献类型：
--
作者：
Li, Ranyang;Pan, Junjun;Qin, Hong
内镜图像中总是存在镜面反射（即高光），它们会严重干扰外科医生的观察和判断。在基于增强现实（AR）的手术导航系统中，高光还可能导致特征提取或配准失败。在本文中，我们提出一种自适应鲁棒主成分分析（Adaptive - RPCA）方法来去除内镜图像序列中的镜面反射。它可以在RPCA分解过程中迭代优化稀疏部分参数。在这种新方法中，我们首先基于像素自适应地检测高光图像。利用所提出的距离度量算法，它然后自动测量稀疏结果图像和检测到的高光图像之间的相似距离。最后，通过使两种类型图像之间的相似距离落在一定范围内来获得低秩和稀疏结果。我们的方法已经通过微创手术（MIS）中多种不同类型的内镜图像序列得到验证。实验和临床盲测表明，新的Adaptive - RPCA方法可以直接获得最优的稀疏分解参数，并且能够产生稳健的高光去除结果。与现有最先进的方法相比，所提出的方法不仅实现了更好的高光去除效果，还能够自适应地处理图像序列。
Specular reflections (i.e., highlight) always exist in endoscopic images, and they can severely disturb surgeons observation and judgment. In an augmented reality (AR)-based surgery navigation system, the highlight may also lead to the failure of feature extraction or registration. In this paper, we propose an adaptive robust principal component analysis (Adaptive-RPCA) method to remove the specular reflections in endoscopic image sequences. It can iteratively optimize the sparse part parameter during RPCA decomposition. In this new approach, we first adaptively detect the highlight image based on pixels. With the proposed distance metric algorithm, it then automatically measures the similarity distance between the sparse result image and the detected highlight image. Finally, the low-rank and sparse results are obtained by enforcing the similarity distance between the two types of images to fall within a certain range. Our method has been verified by multiple different types of endoscopic image sequences in minimally invasive surgery (MIS). The experiments and clinical blind tests demonstrate that the new Adaptive-RPCA method can obtain the optimal sparse decomposition parameters directly and can generate robust highlight removal results. Compared with the state-of-the-art approaches, the proposed method not only achieves the better highlight removal results but also can adaptively process image sequences.