Capillary Blood Flow Measurement based on Nail-fold Microscopic Images using Feature Based Velocity Estimation

Sammanfattning: Microscopic video images of microcirculation have been used in clinical diagnosis for years, and theparameters obtained from images reveal most physiological activities and body organizations.Particularly, the blood flow speed is one of important indexes, which reflects the state ofmicrocirculation and make significant marks in diagnosis.In order to measure capillary blood velocity, a quantity of methods and instruments have beenstudied and developed. Based on the format of measurement, microscopy approaches used widely,can be grouped into two categories. One direct way applies microscopic-imaging technology forvisualization. The other way uses assistant methods such as laser-illumination [1] or labeling RBCswith fluorescein isothiocyanate [2]. In previous study, four methods (Direct Observation Method,Dual-windows Method, Single-window Method, Optical Flow Method) have been studied andanalysed in order to achieve better performance. But still there is a non-negligible deviation inmeasurement within different tries and compared to the data we retrieve from hospital.This study, inspired by previous work, aims to further investigate efficient and reliable algorithms forextracting capillary blood velocity. One possible solution is to implement feature based estimation tocalculate the blood flow speed distribution automatically, point by point along the middle line oftargeting blood vessel. We inherit the idea of generating motion vectors from Optic Flow algorithmwhich has the best accuracy performance in vehicle identification domain. But original optic flowalgorithm makes the system too sophisticated and time consuming. Moreover, its two required basicrules may not stand during the blood flow velocity detection. So a customized feature basedestimation is brought up here and supposed to be a practicable method for analysis not only inaccuracy but also in efficiency. Moreover, this report also introduces picture shifting, red blood cellmotion, and double windows marking to compare and to confirm the results. Previous work will beused as a reference for the assessment of new algorithms.