Automatic Exudate Detection Using Eye Fundus Image Analysis Due to Diabetic Retinopathy

Diabetic retinopathy (damage to the retina) is a disease caused by complications of diabetes, which can eventually lead to blindness. It is an ocular manifestation of diabetes, a systemic disease, which affects up to 80 percent of all patients who have had diabetes for 10 years or more. Despite these intimidating statistics, research indicates that at least 90% of these new cases could be reduced if there was proper and vigilant treatment and monitoring of the patient eyes. The longer a person has diabetes, the higher his or her chances of developing diabetic retinopathy. In this paper, we introduced a new method for eye fundus image analysis, based on exudate segmentation. The proposed algorithm detects the existence of exudates and measures its distribution. In this paper, we classified images of eye fundus into no-exudate or have exudates. This initial classification helps physicians to initiate a treatment process for infected patients. The algorithm is tested using DIARETDB0. The results proved the reliability and robustness of algorithm.


Introduction
Medical image analysis is an area of research that is currently attracting many researchers in all aspect of health and medications (Schneider, Rasband, & Eliceiri, 2012;Walter, Klein, Massin, & Erginay, 2002;Tasman & Jaeger, 2001).This field involves the study of digital images in order to provide computational tools, which will assist the quantification and visualization of interesting pathology and anatomical structures.The progress achieved in this field over recent years has significantly improved the type of medical care that is available to patients.Physicians have advanced diagnostic tools to evaluate their patients' cases in order to plan different forms of medical management and monitor the progress more efficiently (Schneider et al., 2012).In particular, the investigation and the analysis of eye fundus image in the object of determining the impact of diabetes on the vision system is becoming more significant (Walter et al., 2002;Cunha-Vaz, 1998).
Diabetes is a rapidly increasing worldwide problem, which is characterized by defective metabolism of glucose that causes long-term dysfunction and failure of various organs.The most common complications of diabetes are diabetic retinopathy (DR), and diabetic macular edema (DME), which are considered one of the primary causes of blindness and visual impairment in adults (Cunha-Vaz, 1998;Tasman & Jaeger, 2001).The rapid increase of diabetes pushes the limits of the current DR/DME screening capabilities for which the digital imaging of the eye fundus and automatic or semi-automatic image analysis algorithms provide a potential solution.The retina lesions and abnormalities that can be detected using the methods of eye fundus images are hard exudate, soft exudates, microaneurysms, and hemorrhages (Walter et al., 2002;Cunha-Vaz, 1998;Geetan, Acharya, & Ng, 2008).
Exudates appeared as bright yellow-white deposits on the retina due to the leakage of lipid from abnormal vessels (Tasman & Jaeger, 2001).Their shape and size varies with the different retinopathy stages.These lesions are associated with numerous retinal vascular diseases, including diabetic macular edema DME, diabetic retinopathy DR, hypertensive retinopathy, retinal venous obstruction, retinal arterial micoaneurysms, radiation retinopathy, Coat's disease, and capillary hemangioma of the retina (i.e.von Hippel's lesion).Exudation is a risky case because it can lead to severe visual loss when occurring in the central macular region.Thus, such   (3) (4) undus images found as normal, F Pa is the number of normal fundus images found as abnormal, and F Na is the number of abnormal fundus images found as normal (Kauppi et al., 2006).The sensitivity D and specificity D is computed for our proposed algorithm using DIARETDB0.The specificity is 89.2% and the sensitivity is 92.3%.Kauppi et al evaluated sensitivity D and specificity D for algorithm developed by Kuivalainen (2005).The sensitivity D and specificity D was 79%, and 58% respectively.It is very clear that our algorithm performance is better.

Conclusions
Eye fundus image analysis for diabetic disease plays a major role for evaluating the development diabetic retinopathy.This paper introduces a new approach to detect exudates, which is the main component to measure the development of diabetic retinopathy.In our approach, we applied two different techniques to identify exudates.The first one relies on color feature of exudate, and the second approach utilizes edge and boundaries to identify exudates.The proposed algorithm is implemented and tested using a known eye fundus database images (DIARETDB0).The performance of the algorithm is evaluated using sensitivity and specificity.Our algorithm average sensitivity is equal to 92.1%.The average specificity is more than 99%.The results of evaluation showed that the proposed algorithm achieved better results in comparison with recently published algorithms.

Table 2 .
Comparison between our algorithm and recent published approaches utilizing sensitivity measure