Blood vessel extraction from OCT data by short-time RPCA
Optical coherence tomography (OCT) is a medical imaging technology that allows for non-invasive diagnosis of diseases in the early stage. Because blood flow anomalies provide useful information for many diseases, we develop an automatic blood vessel detection algorithm based on the robust principle component analysis (RPCA) technique. Specifically, we propose a short-time RPCA method that divides an OCT volume into segments and decomposes each segment into a low-rank structure representing relatively static tissues and a sparse matrix representing the blood vessels. It efficiently extracts blood vessel structure from OCT data by distinguishing static and dynamic components. This work serves as the foundation for further blood flow analysis.