IEEE Trans Med Imaging. 2018 Aug;37(8):1899-1909. doi: 10.1109/TMI.2018.2834386. Epub 2018 May 8.
Recent advances in optical coherence tomography (OCT) lead to the development of OCT angiography to provide additional helpful information for diagnosis of diseases like basal cell carcinoma. In this paper, we investigate how to extract blood vessels of human skin from full-field OCT (FF-OCT) data using the robust principal component analysis (RPCA) technique. Specifically, we propose a short-time RPCA method that divides the FF-OCT data into segments and decomposes each segment into a low-rank structure representing the relatively static tissues of human skin and a sparse matrix representing the blood vessels. The method mitigates the problem associated with the slow-varying background and is free of the detection error that RPCA may have when dealing with FF-OCT data. Both short-time RPCA and RPCA methods can extract blood vessels from FF-OCT data with heavy speckle noise, but the former takes only half the computation time of the latter. We evaluate the performance of the proposed method by comparing the extracted blood vessels with the ground truth vessels labeled by a dermatologist and show that the proposed method works equally well for FF-OCT volumes of different quality. The average F-measure improvements over the correlation-mapping OCT method, the modified amplitude-decorrelation OCT angiography method, and the RPCA method, respectively, are 0.1835, 0.1032, and 0.0458.
最近光学相干断层扫描（OCT）技术的进步促使 OCT 血管造影技术得以发展，为基底细胞癌等疾病的诊断提供了更多有价值的信息。本文提出了一种基于鲁棒主成分分析（RPCA）的方法，从全场 OCT（FF-OCT）数据中提取人体皮肤的血管结构。具体来说，我们提出了一种基于短时 RPCA 的方法，将 FF-OCT 数据分成若干段，然后对每一段数据进行分解，得到一个低秩矩阵和一个稀疏矩阵，其中低秩矩阵代表相对静态的人体皮肤组织，稀疏矩阵代表血管。该方法解决了背景缓慢变化的问题，避免了 RPCA 处理 FF-OCT 数据时可能出现的检测误差。短时 RPCA 和 RPCA 两种方法都可以从含有强斑点噪声的 FF-OCT 数据中提取血管，而前者的计算时间仅为后者的一半。通过与皮肤科医生标注的血管进行对比，评估了所提方法的性能，结果表明该方法对于不同质量的 FF-OCT 体数据均具有良好的效果。与相关匹配 OCT 方法、改进的振幅相关 OCT 血管造影方法和 RPCA 方法相比，所提方法的平均 F 度量分别提高了 0.1835、0.1032 和 0.0458。
