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. 2016 Feb;35(2):605-11.
doi: 10.1109/TMI.2015.2486619. Epub 2015 Oct 6.

Automated Assessment of Hemodynamics in the Conjunctival Microvasculature Network

Free PMC article

Automated Assessment of Hemodynamics in the Conjunctival Microvasculature Network

Maziyar M Khansari et al. IEEE Trans Med Imaging. .
Free PMC article

Abstract

The conjunctival microcirculation is accessible for direct visualization and quantitative assessment of microvascular hemodynamic properties. Currently available methods to assess hemodynamics in the conjunctival microvasculature use manual or semi-automated algorithms, which can be inefficient for application to a large number of microvessels within the microvascular network. We present an automated image analysis method for measurements of diameter and blood velocity in microvessels. The method was applied to conjunctival microcirculation images acquired in 15 healthy human subjects. Frangi filtering, thresholding, and morphological closing were applied to automatically segment microvessels, while variance filtering was used to detect blood flow. Diameter and blood velocity were measured in arterioles and venules within the conjunctival microvascular network, and blood flow and wall shear rate were calculated. Repeatability and validity of hemodynamic measurements were established. The automated image analysis method allows reliable, rapid and quantitative assessment of hemodynamics in the conjunctival microvascular network and can be potentially applied to microcirculation images of other tissues.

Figures

Fig. 1
Fig. 1
Flow chart depicting steps for automated image registration, vessel segmentation and hemodynamic measurements of the conjunctival microvasculature network.
Fig. 2
Fig. 2
(a) Mean conjunctival microcirculation image generated by averaging consecutive registered image frames; (b) Vessel segmentation by Frangi filtering of the mean image. (c) After removing small objects and a morphological closing operation.
Fig. 3
Fig. 3
Conjunctival microcirculation image displaying detected centerlines after (a) morphological thinning (b) spur removal (c) detection of bifurcations and intersection points (blue dots).
Fig. 4
Fig. 4
(a) Conjunctival microcirculation image displaying the centerlines of two selected vessel segments. (b) SD of intensity values plotted as a function of length for the vessel indicated by the blue centerline. Mean SD (μvessel) (blue horizontal line) is lower than the threshold (Thbackground) (black horizontal line), indicating the lack of discernable blood flow. (c) SD of intensity values plotted as a function of length for the vessel indicated by the red centerline. Mean SD (μvessel) (red horizontal line) is greater than the threshold (Thbackground) (black horizontal line), indicating detectable blood flow. Fig 4 (a) Insert: Spatial-temporal image (STI) generated for the vessel segment indicated by the red centerline. The red line superimposed on the STI displays the calculated slope based on the prominent bands in the STI.
Fig. 5
Fig. 5
Conjunctival microcirculation image displaying vessel boundaries (blue lines) and the magnitude and direction of axial blood velocity (color-coded arrows). Color bar represents velocity in units of mm/s.

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