Fusion of pixel and texture features to detect pathological myopia

Abstract

Myopia is a growing concern in many societies. In extremely high myopia, pathological myopia, which can cause visual loss, can occur. Pathological myopia is also accompanied by various visually perceivable symptoms on the retina, such as peripapillary atrophy. PAMELA is an automatic system for the detection of pathological myopia through the presence of peripapillary atrophy. In this paper, we describe two modules in the PAMELA system based on texture analysis and gray level analysis. A decision engine is then used to fuse the two individual results to obtain an overall analysis. From the results run on a sample batch of images from the Singapore Eye Research Institute, a sensitivity of 0.9 and a specificity of 0.94 with a total accuracy of up to 92.5% is obtained. The promising results indicate good potential for further development of PAMELA as a tool for mass screening for the detection of pathological myopia. This electronic document is a “live” template. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document.