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Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms

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5 Author(s)
Can, A. ; Dept. of Electr. & Comput. Sci. Eng., Rensselaer Polytech. Inst., Troy, NY, USA ; Hong Shen ; Turner, J.N. ; Tanenbaum, H.L.
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Algorithms are presented for rapid, automatic, robust, adaptive, and accurate tracing of retinal vasculature and analysis of intersections and crossovers. This method improves upon prior work in several ways: automatic adaptation from frame to frame without manual initialization/adjustment, with few tunable parameters; robust operation on image sequences exhibiting natural variability, poor and varying imaging conditions, including over/under-exposure, low contrast, and artifacts such as glare; does not require the vasculature to be connected, so it can handle partial views; and operation is efficient enough for use on unspecialized hardware, and amenable to deadline-driven computing, being able to produce a rapidly and monotonically improving sequence of usable partial results. Increased computation can be traded for superior tracing performance. Its efficiency comes from direct processing on gray-level data without any preprocessing, and from processing only a minimally necessary fraction of pixels in an exploratory manner, avoiding low-level image-wide operations such as thresholding, edge detection, and morphological processing. These properties make the algorithm suited to real-time, on-line (live) processing and is being applied to computer-assisted laser retinal surgery.

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Information Technology in Biomedicine, IEEE Transactions on  (Volume:3 ,  Issue: 2 )