Northumbria University has developed Northumbria Research Link (NRL) to enable users to access the University's research output. Copyright © and moral rights for items on NRL are retained by the individual author(s) and/or other copyright owners. Single copies of full items can be reproduced, displayed or performed, and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided the authors, title and full bibliographic details are given, as well as a hyperlink and/or URL to the original metadata page. The content must not be changed in any way. Full items must not be sold commercially in any format or medium without formal permission of the copyright holder. The full policy is available online: http://nrl.northumbria.ac.uk/policies.html This document may differ from the final, published version of the research and has been made available online in accordance with publisher policies. To read and/or cite from the published version of the research, please visit the publisher's website (a subscription may be required.) Abstract-Diabetic retinopathy is considered in terms of the presence of exudates which cause vision loss in the areas affected. This study targets the development of an intelligent mobile-based automatic diagnosis integrated with a microscopic lens to identify retinal diseases at initial stage at any time or place. Exudate detection is a significant step in order obtaining an early diagnosis of diabetic retinopathy, and if they are segmented accurately, laser treatment can be applied effectively. Consequently, precise segmentation is the fundamental step in exudate extraction. This paper proposes a technique for exudate segmentation in colour retinal images using morphological operations. In this method, after pre-processing, the optic disc and blood vessels are isolated from the retinal image. Exudates are then segmented by a combination of morphological operations such as the modified regionprops function and a reconstruction technique. The proposed technique is verified against the DIARETDB1 database and achieves 85.39% sensitivity. The proposed technique achieves better exudate detection results in terms of sensitivity than other recent methods reported in the literature. In future work, our system will be deployed to a mobile platform to allow efficient and instant diagnosis.