Automated Optic Disc region location from fundus images: Using local multi-level thresholding, best channel selection, and an Intensity Profile Model. (May 2019) - Uribe-Valencia, Laura J.; Martínez-Carballido, Jorge F.
Highlights: Use of promising OD location reduces processing area in about 40%. Selects red or green channel with best range for Candidate generation. All images of the datasets are used for testing, given that no training is used. Column-wise intensity model reflecting natural variations of the optic disc zone simplifies classification. The method is image resolution independent by using relative measures. Abstract: Background and objective: Location of optic disc, which corresponds to the visible part of the optic nerve in the eye, is of high importance for bright lesion detection of Diabetic Retinopathy by extracting it and avoiding false positives. Glaucoma detection processes details on the optic disc zone. Location of the macula uses optic disc location as a reference. Thus, the location of optic disc is relevant for several diagnosis procedures on retinal images. Several methods for OD detection in fundus images can be found in the literature; however, the issue is still open to reach better results in terms of accuracy, robustness and complexity. This work provides a simple and image resolution independent method for Optic Disc location for methods that use the optic disc zone elimination or extraction to perform some diagnosis. Methods: This work proposes a simple and reliable method for OD region location in fundus images using four known publicity available datasets: DRIVE, DIARETDB1, DIARETDB0 and e-ophtha-EX. We are introducing an OD region location method based on OD's characteristic high intensity and a novel method for feature's extraction that aims to represent the essential elements that define an optic disc by proposing a model for the pixel intensity variations across the optic disc (column wise). The approach has four main stages: OD pixel region candidate generation, promising OD regions detection, promising candidate features extraction, and classification. All images from the four datasets were used for testing, since no training was used for classification. Results: An OD location accuracy of 99.7% is obtained for the 341 retinal images within the four publicly datasets. Conclusions: The obtained results show that the proposed method is robust and achieves the maximum detection rate in all four compared databases, which demonstrates its effectiveness and suitability to be integrated into a complete prescreening system for early diagnosis of retinal diseases. Use of promising OD region location reduces processing area in about 40%.