Automatic classification of pathological myopia in retinal fundus images using PAMELA

Abstract

Pathological myopia is the seventh leading cause of blindness. We introduce a framework based on PAMELA (PAthological Myopia dEtection through peripapilLary Atrophy) for the detection of pathological myopia from fundus images. The framework consists of a pre-processing stage which extracts a region of interest centered on the optic disc. Subsequently, three analysis modules focus on detecting specific visual indicators. The optic disc tilt ratio module gives a measure of the axial elongation of the eye through inference from the deformation of the optic disc. In the texturebased ROI assessment module, contextual knowledge is used to demarcate the ROI into four distinct, clinically-relevant zones in which information from an entropy transform of the ROI is analyzed and metrics generated. In particular, the preferential appearance of peripapillary atrophy (PPA) in the temporal zone compared to the nasal zone is utilized by calculating ratios of the metrics. The PPA detection module obtains an outer boundary through a level-set method, and subtracts this region against the optic disc boundary. Temporal and nasal zones are obtained from the remnants to generate associated hue and color values. The outputs of the three modules are used as in a SVM model to determine the presence of pathological myopia in a retinal fundus image. Using images from the Singapore Eye Research Institute, the proposed framework reported an optimized accuracy of 90% and a sensitivity and specificity of 0.85 and 0.95 respectively, indicating promise for the use of the proposed system as a screening tool for pathological myopia.