Image processing and machine learning for diagnostic analysis of microcirculation

This study focuses on detection of capillaries and small blood vessels in the videos recorded from the lingual surface using Microscan SDF system. The purpose of this study is to quantitatively monitor and assess the changes that occur in microcirculation during resuscitation period. The results assist physicians in making diagnostically and therapeutically important decisions such as determination of the effectiveness of the resuscitation process. The proposed algorithm applies advanced digital image processing methods to provide quantitative assessment of video signals for detection and characterization of capillaries. The objective of the algorithm is to segment capillaries, estimate the presence and velocity of Red Blood Cells (RBCs), and identify the distribution of blood flow in capillaries for a variety of normal and abnormal cases. The algorithm first, stabilizes each frame to follow the variations in the consecutive frames. Then, time-averaging techniques are applied to the frames to reduce the motion artifact. Histogram equalization, wavelet transform, and median filtering are the subsequent steps applied to accurately detect the blood vessels in each frame. In order to estimate the velocity of RBCs, space time diagrams are obtained through cross-correlation calculations. This study aims to reduce the human interaction as well as the computation time.