Automatic detection of the macula in retinal fundus images using seeded mode tracking approach

Robust Fovea Localization Based on Symmetry Measure

Automatic fovea localization is a challenging issue. In this article, we focus on the study of fovea localization and propose a robust fovea localization method. We propose concentric circular sectional symmetry measure (CCSSM) for symmetry axis detection, and region of interest (ROI) determination, which is a global feature descriptor robust against local feature changes, to solve the lesion interference issue, i.e., fovea visibility interference from lesions, using both structure features and morphological features. We propose the index of convexity and concavity (ICC) as the convexity-concavity measure of the surface and provide a quantitative evaluation tool for ophthalmologists to learn whether the occurrence of lesion within the ROI. We propose the weighted gradient accumulation map, which is insensitive to local intensity changes and can overcome the influence of noise and contamination, to perform refined localization. The advantages of the proposed method lies in two aspects. First, the accuracy and robustness can be achieved without typical sophisticated manner, i.e., blood vessel segmentation and parabola fitting. Second, the lesion interference is considered in our plan of fovea localization. Our proposed symmetry-based method is innovative in the solution of fovea detection, and it is simple, practical, and controllable. Experiment results show that the proposed method can resist the interference of unbalanced illumination and lesions, and achieve high accuracy rate in five datasets. Compared to the state-of-the-art methods, high robustness and accuracy of the proposed method guarantees its reliability.

Integrating research, clinical practice and translation: the Singapore experience

We introduce the experiences of the Singapore ocular imaging team, iMED, in integrating image processing and computer-aided diagnosis research with clinical practice and knowledge, towards the development of ocular image processing technologies for clinical usage with potential impact. In this paper, we outline key areas of research with their corresponding image modalities, as well as providing a systematic introduction of the datasets used for validation.