Learning-Based Visual Saliency Model for Detecting Diabetic Macular Edema in Retinal Image

<table class="figure-group"><tr class="fig-image" id="a"><td><object data="https://static.hindawi.com/articles/cin/volume-2016/7496735/figures/7496735.fig.001a.svgz" name="7496735.fig.001a" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(a) </b>Original image</td></tr><tr class="fig-image" id="b"><td><object data="https://static.hindawi.com/articles/cin/volume-2016/7496735/figures/7496735.fig.001b.svgz" name="7496735.fig.001b" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(b) </b>Eye tracking</td></tr><tr class="fig-image" id="c"><td><object data="https://static.hindawi.com/articles/cin/volume-2016/7496735/figures/7496735.fig.001c.svgz" name="7496735.fig.001c" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(c) </b>Ground truth</td></tr></table>

<div>We collected eye-tracking data on 100 retinal images with diabetic macular edema from 10 ophthalmologists. Gaze tracking paths and fixation locations are recorded in (b). A continuous ground truth (b) is found by convolving a Gaussian over the fixation locations of all users.</div>

Computational Intelligence and Neuroscience

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Figure 1

Figure 1: Learning-Based Visual Saliency Model for Detecting Diabetic Macular Edema in Retinal Image 