Blood vessels overlying one another at distinct depths (and hence appearing to intersect) in the sclera of the eye can be distinguished reliably from those that in fact do branch within the same depth, using only the information contained in a single photograph of the conjunctiva. That conclusion arises from extension of earlier work that qualitatively inferred relative depth of vessels. The current research was motivated by the need to quantify such inferences in terms of their sensitivities and robustness. A physics first principles model forms the basis for selection of features that capture blood vessel depth information. Features extracted from the image are shown to be useful in that effort; their utility is verified with phantoms that mimic the behavior of the conjunctiva and sclera. Because no special preparations are needed, the method works as well on archived images as on newly-acquired ones, and thus can be used in retrospective studies of images of the eye and other diffuse media.
The conjunctiva is an ideal location to study and measure the morphology of the microcirculation, because access to blood vessels at this site is essentially noninvasive. Our efforts have been directed toward automating the labor-intensive process of collecting morphological information from photographs and video images of the conjunctiva. In previous work we have developed a detailed model of the illumination/reflection processes that result in a film or video image of the bulbar conjunctiva. Using information gained from this model, we have now extended our research toward the development of robust microvessel detection and tracking algorithms. Our modeling has shown that it is possible to extract some relative 3D information about blood vessels from their gray-scale profiles. Images of the conjunctiva, however, also exhibit significant variability in their gray-scale data. We have adapted some fuzzy logic concepts to deal with this problem. These fuzzy logic algorithms have been very effective in detecting blood vessel points in these images, and have also been used to link the segments together into blood vessel tracks.
This research is in support of the development of an image processing system which is capable of detecting and tracking blood vessels in photographs or video images of the human microcirculation system. We describe a model which replicates the illumination processes contributing to a film or video image of the microvessels of the human bulbar conjunctiva. The model provides a foundation for microvessel detection algorithms, for measurement of vessel parameters, for determining relative depth of blood vessels, and for separating neighboring vessels in complex images. The model is based on a cylindrical vessel embedded in a diffuse medium which is on a reflecting background. A light source illuminating the scene is reflected by it's components and passes through a pinhole to an image plane, which records these reflections as intensity values at discrete pixel locations. Fundamental physical principles which include Lambert's cosine law, isotropic spreading, Fresnel's law and Beer's law are systematically applied to the model. A video apparatus and a phantom were constructed to analyze different illumination conditions and to verify the model. A simulation based on the model compared favorably with data taken from phantom images.
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