Separation of arteries and veins in the cerebral cortex is of significant importance in the studies of cortical hemodynamics, such as the changes of cerebral blood flow, perfusion or oxygen concentration in arteries and veins under different pathological and physiological conditions. Yet the cerebral vessel segmentation and vessel-type separation are challenging due to the complexity of cortical vessel characteristics and low spatial signal-to-noise ratio. In this work, we presented an effective full-field method to differentiate arteries and veins in cerebral cortex using dual-modal optical imaging technology including laser speckle imaging (LSI) and optical intrinsic signals (OIS) imaging. The raw contrast images were acquired by LSI and processed with enhanced laser speckle contrast analysis (eLASCA) algorithm. The vascular pattern was extracted and segmented using region growing algorithm from the eLASCA-based LSI. Meanwhile, OIS images were acquired alternatively with 630 and 870 nm to obtain an oxyhemoglobin concentration map over cerebral cortex. Then the separation of arteries and veins was accomplished by Otsu threshold segmentation algorithm based on the OIS information and segmentation of LSI. Finally, the segmentation and separation performances were assessed using area overlap measure (AOM). The segmentation and separation of cerebral vessels in cortical optical imaging have great potential applications in full-field cerebral hemodynamics monitoring and pathological study of cerebral vascular diseases, as well as in clinical intraoperative monitoring.
The Spectral Analysis of Dynamic Laser Speckle Patterns Generated by Brownian Particle Suspensions: A Stroboscopic Effect Based Filtering Technique
