Visual dictionary: A decision support tool for DR pathology detection on POI

Abstract

The visual word dictionary is based on measuring metrics at the image level and does not follow a “decision-support tool” approach, where the system is used to aid diagnosis, as it is found in most of the current methods. The decision-support approach requires pinpointing the location of each lesion to allow the specialist to evaluate the image for diagnosis. In this case, metrics based on the accuracy of detecting each type of lesion is more relevant. Using a visual words dictionary as the basis for a classification algorithm was inspired by the computer vision, where visual dictionaries and PoIs are used as a basis for several applications such as image retrieval and classification. The visual word that combines feature information contained within the images in a framework easily extendible to different types of retinal lesions or pathologies and builds a specific projection space for each class of interest (e.g., white lesions such as exudates or normal regions) instead of a common dictionary for all classes. The visual words dictionary was applied to classifying bright and red lesions with classical cross validation and cross dataset validation to indicate the robustness of this approach. The curve (AUC) of 95.3% for white lesion detection and an AUC of 93.3% for red lesion detection using fivefold cross validation and data consisting of 687 images of normal retinae, 245 images with bright lesions, 191 with red lesions, and 109 with signs of both bright and red lesions. The image classification resulted in an AUC of 88.1% when classifying the RetiDB dataset and in an AUC of 89.3% when classifying the Messidor dataset, both cases for bright lesion detection. The results indicate the potential for training with different acquisition images under different setup conditions with a high accuracy of referral based on the presence of either red or bright lesions or both.