Optic Disk Segmentation Using Histogram Analysis

In the field of disease diagnosis with ophthalmic aids, automatic segmentation of the retinal optic disc is required. The main challenge in OD segmentation is to determine the exact location of the OD and remove noise in the retinal image. This paper proposes a method for automatic optical disc segmentation on color retinal fundus images using histogram analysis. Based on the properties of the optical disk, where the optical disk tends to occupy a high intensity. This method has been applied to the Digital Retinal Database for Vessel Extraction (DRIVE)and MESSIDOR database. The experimental results show that the proposed automatic optical segmentation method has an accuracy of 55% for DRIVE dataset and 89% for MESSIDOR databas

A Novel Approach for the Detection & Classification of Diabetic Retinopathy

When sugar level(glucose) in the blood fails to regulate the insulin properly in human body ,diabetic is occurred. The effect of diabetic on eye causes diabetic retinopathy. Diabetic Retinopathy is one of a complicated diabetes which can cause blindness .It is a metabolic disordered. patients perceive no symptoms until the disease is at late stage. So early detection and proper treatment has to be ensured. To serve this purpose, various automated systems have been designed. There are two levels of diabetic retinopathy which are non proliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR).The presence of micro aneurysms in the eye is one of the early signs of diabetic retinopathy. The objectives of this paper are 1) classify different stages of non proliferative diabetic retinopathy (NPDR) as mild NPDR, Moderate NPDR, Severe NPDR. 2)classification of micro aneurysms and exudates. DOI: 10.17762/ijritcc2321-8169.15031

Blood Vessel Enhancement and Segmentation for Screening of Diabetic Retinopathy

Diabetic retinopathy is an eye disease caused by the increase of insulin in blood and it is one of the main cuases of blindness in idusterlized countries. It is a progressive disease and needs an early detection and treatment. Vascular pattern of human retina helps the ophthalmologists in automated screening and diagnosis of diabetic retinopathy. In this article, we present a method for vascular pattern ehnacement and segmentation. We present an automated system which uses wavelets to enhance the vascular pattern and then it applies a piecewise threshold probing and adaptive thresholding for vessel localization and segmentation respectively. The method is evaluated and tested using publicly available retinal databases and we further compare our method with already proposed techniques.

Detection of Hard Exudates in Retinal Fundus Images using Deep Learning

Diabetic Retinopathy (DR) is a retinal disorder that affects the people having diabetes mellitus for a long time (20 years). DR is one of the main reasons for the preventable blindness all over the world. If not detected early the patient may progress to severe stages of irreversible blindness. Lack of Ophthalmologists poses a serious problem for the growing diabetes patients. It is advised to develop an automated DR screening system to assist the Ophthalmologist in decision making. Hard exudates develop when DR is present. It is important to detect hard exudates in order to detect DR in an early stage. Research has been done to detect hard exudates using regular image processing techniques and Machine Learning techniques. Here, a deep learning algorithm has been presented in this paper that detects hard exudates in fundus images of the retina.Comment: 5 Pages, 3 figures, 2 tables, International Conference on Systems, Computation, Automation and Networking http://icscan.in

Detection of Macula and Recognition of Aged-Related Macular Degeneration in Retinal Fundus Images

In aged people, the central vision is affected by Age-Related Macular Degeneration (AMD). From the digital retinal fundus images, AMD can be recognized because of the existence of Drusen, Choroidal Neovascularization (CNV), and Geographic Atrophy (GA). It is time-consuming and costly for the ophthalmologists to monitor fundus images. A monitoring system for automated digital fundus photography can reduce these problems. In this paper, we propose a new macula detection system based on contrast enhancement, top-hat transformation, and the modified Kirsch template method. Firstly, the retinal fundus image is processed through an image enhancement method so that the intensity distribution is improved for finer visualization. The contrast-enhanced image is further improved using the top-hat transformation function to make the intensities level differentiable between the macula and different sections of images. The retinal vessel is enhanced by employing the modified Kirsch's template method. It enhances the vasculature structures and suppresses the blob-like structures. Furthermore, the OTSU thresholding is used to segment out the dark regions and separate the vessel to extract the candidate regions. The dark region and the background estimated image are subtracted from the extracted blood vessels image to obtain the exact location of the macula. The proposed method applied on 1349 images of STARE, DRIVE, MESSIDOR, and DIARETDB1 databases and achieved the average sensitivity, specificity, accuracy, positive predicted value, F1 score, and area under curve of 97.79 %, 97.65 %, 97.60 %, 97.38 %, 97.57 %, and 96.97 %, respectively. Experimental results reveal that the proposed method attains better performance, in terms of visual quality and enriched quantitative analysis, in comparison with eminent state-of-the-art methods

EARLY DIAGNOSIS OF DIABETIC RETINOPATHY BY THE DETECTION OF MICROANEURYSMS IN FUNDUS IMAGES

The detection of microaneurysms is crucial, as it is an early indicator of a complication of prolonged diabetes called Diabetic Retinopathy. In this paper, an automated approach is proposed to detect microaneurysms from retinal fundus images. Firstly, the magenta plane of the input image is extracted and a few preprocessing techniques are carried out. This is followed by the localization and the removal of the optic disk. The threshold value is determined and is optimized using Firefly algorithm. Then top hat transform is applied to detect the microaneurysms. The image quality parameters and the performance parameters were calculated and analyzed on the images of the DIARETDB1 database. The experimental results yielded a sensitivity of 99.80% before optimization and 100% after optimization

Segmentation and texture analysis with multimodel inference for the automatic detection of exudates in early diabetic retinopathy

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Unsupervised spike sorting for large-scale, high-density multielectrode arrays

electrophysiology; high-density multielectrode array; neural cultures; retina; spike sortin