IMAGE PROCESSING AND MACHINE LEARNING FOR DIAGNOSTIC ANALYSIS OF MICROCIRCULATION

摘要

Automated quantitative analysis of microcirculation, such as density of blood vessels and red blood cell velocity, is implemented using image processing and machine learning techniques. Detection and quantification of the microvaseulature is determined from images obtained through intravital microscopy. The results of quantitatively monitoring and assessing the changes that occur in microcirculation during resuscitation period assist physicians in making diagnostic-ally and therapeutically important decisions such as determination of the degree of illness as well as the effectiveness of the resuscitation process. Advanced digital image processing methods are applied to provide quantitative assessment of video signals for detection and characterization of the microvasculature (capillaries, venules, and arterioles). The microvasculature is segmented, the presence and velocity of Red Blood Cells (RBCs) is estimated, and the distribution of Wood flow in capillaries is identified for a variety of normal and abnormal cases.