© IEOM Society Review of Image Processing Techniques for Detection of Age- related Macoular Degeneration (ARMD) Literature Review Samina Khalid Computer Science and Information Technology Department, Mirpur University of Science and Technology Mirpur AJK Pakistan noshi_mir@yahoo.com Dr. M. Usman Ikram College of E&ME, NUST Rawalpindi, Pakistan usman.akram@ceme.nust.edu.pk Abstract Detection of retinal drusen is important in the diagnosis of Age-related Macular Degeneration (ARMD). Automated image processing has the potential to assist in the early detection of ARMD, by changing in blood vessels and pattern in retina. Age- related macular degeneration cause gradual loss of vision by oxidation of macula and can cause irreversible vision loss. The main goal of the proposed system is twofold at first it is used to diagnose the type of age-related macular degeneration either it is dry macular degeneration or wet macular degeneration, and then further helps to classify the stage of dry macular degeneration into early, intermediate, or advanced dry ARMD and to automatically detect and segment ARMD without human supervision. Detection of ARMD is done by using Auto Associative Neural Network (AANN) method and the two classes of Age-related macular degeneration (dry or wet ARMD), one of which dry macular can be further classify into three classes, will be classify and diagnose successfully as future work. Keywords: Fundus Images, OCT Images, Drusen, Macular Degeneration, ARMD. INTRODUCTION Age-related macular degeneration (ARMD) the leading cause of worldwide blindness in the elderly age is a bilateral ocular condition that affects the central area of retina known as the macula. Although the macula comprises only four percent of retinal area, it is responsible for the majority of useful photonic vision [1]. ARMD is the main cause of the elderly blindness in developed countries e.g. Australia, United Kingdom, and America. According to a survey approximately 17% of the participants were diagnosed with ARMD; further, more than 95% of these were aged 60 years and above [2]. In United States ARMD is also a growing public health problem, almost 11 million, or 7.6% of all Americans are estimated to have ARMD, and it is the cause of blindness for 54% of all legally blind Americans. ARMD is a major societal problem in terms of disability and health care costs. For example, severe ARMD reduces the likelihood of employment by 61% and salary by 39%, while mild ARMD reduces these by 44% and 32% respectively. The estimated annual cost burden from ARMD in the U.S. is $30 billion (USD) or about 0.3% of gross domestic product [3]. The occurrence of ARMD is expected to double over the next 25 years. ARMD caused due to deposits of bright lesions called drusen. Drusen are formed at retinal level and could affect eyesight. Many researches have been done in the field of medical care. The diagnosis of ARMD is typically undertaken through the inspection of the macula (see Figure:1). Manual recognition and detection of drusen from retinal images is time consuming and expensive. Moreover, it is subjective and its reproducibility is a concern. To save workload and facilitate large-scale clinical use, it is important to have a precise, cost effective and efficient system to detect drusen automatically for ARMD diagnosis [4]. ARMD Proceedings of the 2015 International Conference on Operations Excellence and Service Engineering Orlando, Florida, USA, September 10-11, 2015 407