Depth from physics: develpoment of a robust classifier for 2D image analysis

David W. Hattery; Murray H. Loew; Carl E. Wick

doi:10.1117/12.310956

24 June 1998 Depth from physics: develpoment of a robust classifier for 2D image analysis

David W. Hattery, Murray H. Loew, Carl E. Wick

Proceedings Volume 3338, Medical Imaging 1998: Image Processing; (1998) https://doi.org/10.1117/12.310956
Event: Medical Imaging '98, 1998, San Diego, CA, United States

Abstract

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.

Citation Download Citation

David W. Hattery, Murray H. Loew, and Carl E. Wick "Depth from physics: develpoment of a robust classifier for 2D image analysis", Proc. SPIE 3338, Medical Imaging 1998: Image Processing, (24 June 1998); https://doi.org/10.1117/12.310956

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