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20 March 2014 An image-based software tool for screening retinal fundus images using vascular morphology and network transport analysis
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As the number of digital retinal fundus images taken each year grows at an increasing rate, there exists a similarly increasing need for automatic eye disease detection through image-based analysis. A new method has been developed for classifying standard color fundus photographs into both healthy and diseased categories. This classification was based on the calculated network fluid conductance, a function of the geometry and connectivity of the vascular segments. To evaluate the network resistance, the retinal vasculature was first manually separated from the background to ensure an accurate representation of the geometry and connectivity. The arterial and venous networks were then semi-automatically separated into two separate binary images. The connectivity of the arterial network was then determined through a series of morphological image operations. The network comprised of segments of vasculature and points of bifurcation, with each segment having a characteristic geometric and fluid properties. Based on the connectivity and fluid resistance of each vascular segment, an arterial network flow conductance was calculated, which described the ease with which blood can pass through a vascular system. In this work, 27 eyes (13 healthy and 14 diabetic) from patients roughly 65 years in age were evaluated using this methodology. Healthy arterial networks exhibited an average fluid conductance of 419 ± 89 μm3/mPa-s while the average network fluid conductance of the diabetic set was 165 ± 87 μm3/mPa-s (p < 0.001). The results of this new image-based software demonstrated an ability to automatically, quantitatively and efficiently screen diseased eyes from color fundus imagery.
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Richard D. Clark, Daniel J. Dickrell, and David L. Meadows "An image-based software tool for screening retinal fundus images using vascular morphology and network transport analysis", Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90350D (20 March 2014);

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