The human eye, that reacts to light and pressure is one among the most compounds organ in our body. Though small in size, it is constituted by different structres that makes vision possible. There are three layers which constitute human eye. Sclera is the outer layer that protects the eye ball. Cornea is a transparent membrane which forms the outer coating.
It will provide a protection to iris and pupil. The main function is to focus light onto the retina. Beneath sclera, the second layer exists which is called choroid.
It is a vascular layer which has blood vessels and is responsible for providing nourishment to the eye. Beneath choroid, there exists retina. Retina is the inner most layer of eye. Retina is responsible for eye vision and pigmentation with the presence of photoreceptors 1. The macula is the small area at the centre of the retina and is responsible for central vision. It determines what we see straight in front of us, at the centre of our field of vision. It is a very important part as it gives us the vision needed for basic activities such as reading and writing, and the ability to appreciate color etc 2. It is an oval-shaped pigmented area near the center of the retina, with a diameter of approximately 5.
5 mm in human eyes 2. Near the center of the macula is a tiny dip packed with light-sensitive cells called fovea 3. The fovea picks up the finest details of central vision. The basic structure of human Eye and macular are shown in Figure 1 and Figure 2 respectively.
A. Macular Disorders Macular disorders mainly damage the macular region of the retina, and can affects the central vision of a person 3. There are different types of macular disorders, but some common ones are Macular Edema (ME), Central Serous Retinopathy (CSR), Macular Hole (MH), Age related Macular Degeneration (AMD), Choroidal Neovascularization, Pigment Epithelium Detachment and the Förster–Fuchs retinal spot 3. Most of these macular disorders are curable if diagnosed at an early stage. The common symptoms of macular impairments includes 1) Issues with the central vision: The central vission gets obstructed or unclear due to certain blurred patch.The affected will face difficulties while doing the basic things like reading, driving etc..2) Metamorphopsia :Distortion of images ,especially of straight lines is characterised by Metamorphopsia 5.
3) Distortion of image size: Macropsia and Micropsia, Objects may appear bigger or smaller respectively. This may in turn give rise to diplopia, a discrepancy between the image perceived by a healthy eye and a diseased one. In the case of ME, the retinal layers are swollen due to the leakage of fluid from retinal blood vessels. There are two major causes of ME. The first one is diabetes, where small blood capillaries within the retina start leaking fluid 2. In this case, ME is termed diabetic macular edema (DME) 2.
Eye (cataract) surgery may also increase the risk of developing macular edema due to irritated blood vessels and fluid leakage 3. Such an ME is termed cystoid macular edema (CME). CSR occurs due to the accumulation of serous fluid beneath the retina and causes the retinal layers to detach 3. There are two types of CSR. In Type 1 CSR, the fluid accumulates under the neurosensory retina.
Type II is characterized by the accumulation of fluid in the retina due to retinal pigment epithelium (RPE) leakage. Serous fluid in such cases tends to be shallower rather than domed shaped. Common symptoms of ME and CSR are dim, blurred, and distorted central vision 2.There are multiple techniques that are used to detect retinal disorders. Some common techniques are fundus photography, fundus fluorescein angiography and OCT 2. OCT is the recently developed technology to detect macular diseases such as ME, Macular Hole, Central Serous Retinopathy, Choroidal Neovascularization and Pigment Epithelium Detachment. The principle advantage of using OCT images is that it can assist in the detection of the diseases in the very earlier stages using appropriate techniques 3. B.
Optical Coherence Tomography: Optical Coherence Tomography, or OCT technology, allows optometrist to take cross sectional images of the retina, commonly referred as OCT scan 1. It’s a medical imaging technique that uses light to capture micrometer-resolution, three-dimensional images from within optical scattering media like biological tissues. It is based on low coherence interferometry and utilizes near infrared light.
It employs the principle of Michelsons interferometer 2.OCT imaging technology mainly consists of OCT camera which uses a low coherence interferometry in which low coherence visible light is allowed to penetrate human retina and it is reflected back to interferometer producing a cross sectional image of retina. The OCT imaging technique and OCT images of the macula are shown in figure 4 and figure5 respectively.II. LITERATURE REVIEW Thissection review the existing methods, which gives more details about automaticdetection .Most of the existing systems are based on figure 6.
Pre processing i Segmentation Feature extraction Feature set formulation Classification ME detection Input OCT images Fig. 6. Common systemsfor detection of MEA. Detection using Fundus photography Fundus photography is used to capture animage of the back of the eye that is fundus. It gives the three dimensionalimage of the retina and choroidal tissues. Ophthalmologists make use of thefundus images to detect and diagnose patients with ME.
This approach is highlyuseful and accurate, provided there is a sharp fundus image obtained. But itrequires a sharp and well focused fundus camera image. To make proper judgmentsthe ophthalmic photographer must be well versed with the layered anatomy of theposterior pole and its relationship to the disease. The adjustment of funduscamera eyepiece and the different step to be followed for focusing the funduscamera is relatively a difficult task. The coordination among the photographer,patient, and camera determines the final sharpness of the image. Specialtechniques must be designed for optimizing the sharpness in stereo images. Thedetection of macular disorders using the fundus images is therefore not afeasible option since it contains several parameters and predefined knowledgethat determines the accuracy of results.
This approach guarantees a sensitivityof 100% and specificity ranging between 74% and 90% 10. B. Detection usingTexture features and Classification using SVM Classifier This approach concentrates on bringingdown the computational time. It utilizes a computerized method for texturefeature extraction. The extraction is carried within a specified radius withmacula as the centre. Proper segmentation techniques must be applied to extractthe features. The texture features can vary greatly since the extracted regioncan contains a great amount of abnormalities like micro-aneurysms,hard-exudates and hemorrhages. This methodology can be used to classifydiabetic macular edema into stage 0(Normal) and stage 2 (Abnormal) based on the extracted features.
Accompanyingthe texture feature extraction, it utilizes a Support Vector Machine (SVM)classifier for grading. Sensitivity,Specificity and Accuracy are the parameters that determine the performance ofthis system. Experimental results have proved that it can provide 91%, 75% and86 % Sensitivity, Specificity and Accuracy respectively 8. C. Detection of diabetic retinopathy with image processing Diabetic retinopathy can be detected bythe application of image processing techniques on the color fundus images. Itcan be done through image enhancement, mass screening that includes detectionof pathologies and retinal features. It is followed by monitoring stage. Thefeature detection and registration of retinal images is carried out during thisstage.
To diagnose the disease properly, it needs the detection of exudates.But this approach cannot distinct between hard exudates and soft Exudates withthe proposed technique. Hence the application of the image processing techniquein detecting Macular Edema is limited 6. D. Detection using automated feature extractiontechnique. This technique focuses on localizing thedifferent features and lesions extracted from the fundus retinal image. Itplaces a constraint for optic disk detection where we first detect the majorblood vessels and use the intersection of these blood vessels to determine theapproximate location of the optic disk, which can be further assisted by the colorproperties.
The region around the optic disk can be further divided into fourquadrants in order to determine the severity of the disorders. The application ofthesis approach on a database containing 516 images with varied contrast,illumination and disease stages posses 97.1% success rate for optic disklocalization 6.
E. Detectionof ME on the based on OCT This approach utilizes the OCT retinal imagesalong on the Support Vector machine classifier to detect Macular Edemaautomatically. The Support Vector is trained using the distinct featuresextracted from labeled images with macular disorders. The experiments werecarried on a local dataset acquired from AFIO and the results states that thealgorithm worked 88 out of 90 times. It provides an accuracy rate of 97.77 %,sensitivity of 100 % and specificity of 93.33%. This approach reduces thecomputational time to a significant level 1.
III. RESULTS AND DISCUSSION Thereexist different approaches to detect Macular Edema. The different methods andthe experimental results are shown in the table I. The main parameters thatdetermine the effectiveness of a method are accuracy, sensitivity and specificity.Based on the calculations, it can be noticed that Bilal Hassan et.al achieves the best results with97.
77% of accuracy, which is the structure tensor based Macular Edema andCentral Serous Retinopathy detection using the OCT images. A Support VectorMachine classifier is trained with distinct features that are extracted fromnumerous labeled images. Additionally it provides 93.35 % of specificity and100% sensitivity. It requires computationally less time and is quitefast. Annu Anna Lal et al are not behind with 97.1 % accuracy rate.
Theresults becomes better based on the algorithm used for segmentation, featureextraction, feature set formulation and classification which forms the backboneof any technique. TABLE I: Result analysesof existing methods Subjects Specifications Accuracy Specificity Sensitivity Aditya Kunwar et.al8 86% 75% 91% Annu Anna Lal et.al6 97.1% —– —– Bilal Hassan et.
al1 97.77% 93.35% 100% IV. CONCLUSION AND FUTURE SCOPEInthis paper we discussed on the computerized detection of the various macular orretinal diseases.
Various techniques can be employed to detect the Macularedema. From the analysis it is clear that the system proposed by Bilal Hassanet.al 1 can detect ME with about 98 percent accuracy and 100 percentsensitivity. But the earlier automatic and accurate detection is not defined sofar. Additionally, the major issue with the macular edema is that if they arenot diagnosed and treated at the initial stages then the chances of therecovery is very feeble. Hence it demands an algorithm which can be deployed onthe OCT images of a retina to detect and classify the ME at early stages. Wehave reviewed several techniques that can be utilized to detect ME and reacheda conclusion that the OCT image when treated with SVM provides much betterresults.
Going forward in future we can develop, 1) A single algorithm that can be used todetect all the types of macular disorders at one go such as, tractional retinaldetachment, PEDs, and choroidal neovascularization in one go. 2) An algorithm should be developed for thegrading of the common retinal diseases. 3) Additionally an approach can be made whichcalculates the thickness level between the ILM and RPE from circular opticnerve head scans in order to detect the ocular diseases.