Robust Filtering Based Segmentation and Analysis of Dura Mater Vasculature using Epifluorescence Microscopy

We show that an adaptive robust filtering based image segmentation model can be used for microvasculature network detection and quantitative characterization in epifluorescence-based high resolution images. Inhomogeneous fluorescence contrast due to the variable binding properties of the lectin marker and leakage hampers accurate vessel segmentation. We use a robust adaptive filtering approach to remove noise and reduce inhomogeneities without destroying small scale vascular structures. An adaptive variance based thresholding method combined with morphological filtering yields an effective detection and segmentation of the vascular network suitable for medial axis estimation. Quantitative parameters of the microvascular network geometry, including curvature, tortuosity, branch segments and branch angles are computed using post segmentation-based medial axis tracing. Experiments using epifluorescence-based high resolution images of porcine and murine microvasculature demonstrates the effectiveness of the proposed approach for quantifying morphological properties of vascular networks.