P.-H. LeeC.-C. ChanS.-L. HuangA. ChenH. H. ChenSHENG-LUNG HUANG2019-10-312019-10-3120182780062https://scholars.lib.ntu.edu.tw/handle/123456789/429387Recent 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. ? 2018 IEEE.[SDGs]SDG3Angiography; Matrix algebra; Medical imaging; Optical tomography; Principal component analysis; Quality control; Skin; Biomedical imaging; Blood flow; Blood vessel detection; Face; Red blood cell; Robust principal component analysis; Sparse matrices; Blood vessels; accuracy; adult; anatomical concepts; Article; correlation coefficient; evaluation study; face; forearm; human; image analysis; image processing; image quality; male; optical coherence tomography; optical coherence tomography angiography; principal component analysis; robust principal component analysis; skin; skin blood vessel; tissue structure; algorithm; angiography; blood vessel; diagnostic imaging; image processing; optical coherence tomography; principal component analysis; procedures; skin; vascularization; Adult; Algorithms; Angiography; Blood Vessels; Humans; Image Processing, Computer-Assisted; Male; Principal Component Analysis; Skin; Tomography, Optical Coherencejournal article10.1109/tmi.2018.28343862-s2.0-85046748365