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

This paper brings forth a learning-based visual saliency model method for detecting diagnostic diabetic macular edema (DME) regions of interest (RoIs) in retinal image. The method introduces the cognitive process of visual selection of relevant regions that arises during an ophthalmologist's image examination. To record the process, we collected eye-tracking data of 10 ophthalmologists on 100 images and used this database as training and testing examples. Based on analysis, two properties (Feature Property and Position Property) can be derived and combined by a simple intersection operation to obtain a saliency map. The Feature Property is implemented by support vector machine (SVM) technique using the diagnosis as supervisor; Position Property is implemented by statistical analysis of training samples. This technique is able to learn the preferences of ophthalmologist visual behavior while simultaneously considering feature uniqueness. The method was evaluated using three popular saliency model evaluation scores (AUC, EMD, and SS) and three quality measurements (classical sensitivity, specificity, and Youden's J statistic). The proposed method outperforms 8 state-of-the-art saliency models and 3 salient region detection approaches devised for natural images. Furthermore, our model successfully detects the DME RoIs in retinal image without sophisticated image processing such as region segmentation.