IEEE Trans Med Imaging. 2020 Feb;39(2):328-340. doi: 10.1109/TMI.2019.2926501. Epub 2019 Jul 3.
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.
镜面反射（即高光）总是存在于内窥镜图像中，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，高光也可能导致特征提取或配准失败。在本文中，我们提出了一种自适应鲁棒主成分分析（Adaptive-RPCA）方法来去除内窥镜图像序列中的镜面反射。它可以在 RPCA 分解过程中迭代地优化稀疏部分参数。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。然后，使用提出的距离度量算法，它自动测量稀疏结果图像和检测到的高光图像之间的相似性距离。最后，通过强制两种图像之间的相似性距离落在一定范围内，得到低秩和稀疏结果。我们的方法已经在微创手术（MIS）中的多种不同类型的内窥镜图像序列中得到了验证。实验和临床盲测表明，新的自适应 RPCA 方法可以直接获得最优的稀疏分解参数，并生成稳健的高光去除结果。与最先进的方法相比，所提出的方法不仅可以实现更好的高光去除效果，而且可以自适应地处理图像序列。
