Pathological myopia is the seventh leading cause of blindness worldwide. Current methods for the detection of pathological myopia are manual and subjective. We have developed a system known as PAMELA (Pathological Myopia Detection Through Peripapillary Atrophy) to automatically assess a retinal fundus image for pathological myopia. This paper focuses on the texture analysis component of PAMELA which uses texture features, clinical image context and support vector machine-based classification to detect the presence of pathological myopia in a retinal fundus image. Results on a test image set from the Singapore Eye Research Institute show an accuracy of 87.5% and a sensitivity and specificity of 0.85 and 0.90 respectively. The results show good promise for PAMELA to be developed as an automatic tool for pathological myopia detection.