Optic Disc

Now a days computer aided design and diagnosis is very popular. Most of the diseases screening and detection is performed with the help of a computer. Diabetic retinopathy is one of the diabetic eye diseases found in the patients who have diabetic in last 20-30 years. The main objective of this work is to effectively found diabetic retinopathy those who have diabetic by using Hough transform and bottom hat transform. Selection of the needed region and extract the decided feature is very important in CAD. Hough transform is one of the best method for feature extraction. It follows voting procedure for feature extraction. The purpose of the technique is to find imperfect instances of objects within a certain class of shapes.

This paper proposes a method for the Retinal image analysis through efficient detection of exudates and recognizes the retina to be normal or abnormal. The contrast image is enhanced by curvelet transform. Hence, morphology operators are applied to the enhanced image in order to find the retinal image ridges.
A simple thresholding method along with opening and closing operation indicates the remained ridges belonging to vessels. The clustering method is used for effective detection of exudates of eye. Experimental result proves that the blood vessels and exudates can be effectively detected by applying this method on the
retinal images. Fundus images of the retina were collected from a reputed eye clinic and 110 images were trained and tested in order to extract the exudates and blood vessels. In this system we use the Probabilistic Neural Network (PNN) for training and testing the pre-processed images. The results showed the retina is
normal or abnormal thereby analyzing the retinal image efficiently. There is 98% accuracy in the detection of the exudates in the retina.

Glaucoma is a serious eye disease, overtime it will result in gradual blindness. Early detection of the disease will help prevent against developing a more serious condition. A vertical cup-to-disc ratio which is the ratio of the vertical diameter of the optic cup to that of the optic disc, of the fundus eye image is an important clinical indicator for glaucoma diagnosis. This paper presents an automated method for the extraction of optic disc and optic cup using Fuzzy C Means clustering technique combined with thresholding. Using the extracted optic disc and optic cup the vertical cup-to-disc ratio was calculated. The validity of this new method has been tested on 365 colour fundus images from two different publicly available databases DRION, DIARATDB0 and images from an ophthalmologist. The result of the method seems to be promising and useful for clinical work.

Eye disease identification techniques are highly important in the field of ophthalmology. A vertical Cup-to-Disc Ratio which is the ratio of the vertical diameter of the optic cup to that of the optic disc, of the fundus eye image is one of the important signs of glaucoma. This paper presents an automated method for the extraction of optic disc and optic cup using Fuzzy C Means clustering technique. The validity of this new method has been tested on 454 colour fundus images from three different publicly available databases DRION, DIARATDB0 and DIARETDB1 and, images from an ophthalmologist. The average success rate of optic disc and optic cup segmentation is 94.26percentage. The scatter plot depicts high positive correlation between clinical CDR and the CDR obtained using the new method. The result of the system seems to be promising and useful for clinical work.

Early perception and confinement of optic disc are one of the crucial steps to detect the various diseases like Diabetic Retinopathy, Glaucoma and many other. Detection of optic disc in an exact way is very important in diabetic retinopathy wherein the retina the weak vessels start developing. Each of the vessels in the retina arises from the optic disc and each of them pursue the same directional pattern which is parabolic in nature. Normally Optic disc is circular in shape and it is set down 3 to 4mm to the higher part of the fovea. Diabetic Retinopathy is a disease which affects much of people having a high blood sugar level which will harm the blood vessels present in the retina. Early diagnosis of diabetic retinopathy is very important for which detection of Optic disc plays a very important role. With the help of various transforms like Discrete Wavelet Transform (DWT), Krisch Transform, Bottom Hat Transform can be done in a proper manner. By using DRIVE datasets which is publicly available the suggested method is being evaluated.

The basis of study and analysis of various eye diseases includes Optic disc(OD) nerve head region and OD center coordinates. The early detection of various eye pathologies like glaucoma and Diabetic Retinopathy helps to prevent the vision loss. So, there is a need to develop a fast and efficient algorithm for disease prediction. For that, reliable and efficient OD localization and segmentation are the important tasks. Therefore, this method aims for the efficient and automatic localization and segmentation of Optic Disc from digital fundus images and its peripappilary atropy detection to predict whether the optic disc is affected by any diseases like glaucoma. The proposed technique is divided into three subsections which deal with OD localization, OD boundary detection and peripappilary atropy Detection (PPA). OD localization makes use of the unique circular brightness structure associated with the OD, that is, the OD usually has a circular shape and is brighter than the surrounding pixels whose intensity becomes darker gradually with their distances from the OD center. OD boundary detection includes accurate blood vessels inpainting for the removal of vascular structure in the optic disc region which is followed by intensity adjustment and region growing considering OD center as a seed point for reliable segmentation of fundus images. The presence of PPA indicates whether the eye is affected by the diseases like glaucoma. So from the segmented optic disc, PPA is detected using a threshold so that we can predict whether the person is affected by disease or not. This is done by calculating the Red by Green ratio for each pixel in the Region of Interest (ROI). The process is implemented in a MATLAB 2014 prototype and tested with images in the DRIVE Dataset. The results show that the suggested method has 82.14% of accuracy, 75% of precision and 81.82% of recall for segmentation and PPA prediction.

Purpose: To determine factors associated with the test-retest variability of
optic nerve head (ONH) topography measurements with confocal scanning
laser ophthalmoscopy (CSLO) in newly diagnosed glaucomatous patients.
Methods: Consecutive patients with newly diagnosed primary open-angle
glaucoma were prospectively enrolled. Patients presenting with any ocular
disease other than glaucoma were excluded. All patients underwent CSLO
using the Heidelberg Retina Tomograph III (HRT-III) in one randomly selected
eye (three consecutive scans; performed by the same examiner). For each
Heidelberg Retina Tomograph III parameter, repeatability was assessed using
within subject standard deviation (Sw) and coefficient of variation (CVw), repeatability
coefficient (RC) and intraclass correlation coefficient (ICC). Scatter
plots and regression lines were constructed to identify which factors influenced
test-retest measurement variability.
Results: A total of 32 patients were included (mean age, 65.4 ± 13.8 years).
Most patients were female (65%) and white (50%). Among all Heidelberg
Retina Tomograph III parameters evaluated, rim area and mean cup depth had
the best measurement repeatability. Vertical cup-to-disc ratio (CDR, as determined
by optic disc stereophotograph examination) was significantly associated
(R2=0.21, p<0.01) with test-retest measurement variability. Eyes with larger
CDR showed less variable measurements. Other factors, including age, disc
area, central corneal thickness and intraocular pressure were not significant
(p>0.14).
Conclusion: Heidelberg Retina Tomograph III showed good test-retest repeatability
for all ONH topographic measurements, mainly for rim area and
mean cup depth. Test-retest repeatability seemed to improve with increasing
CDR. These findings suggest that HRT-III topographic measurements should
be cautiously interpreted when evaluating longitudinally glaucoma patients
with early structural damage (small CDR).

Locating the optic disc center and the fovea in digital fundus images is surprisingly  difficult due to the variation range in color and contrast and the possible presence of  pathologies creating bright spots or changing the appearance of retinal landmarks. These  reasons make it difficult to find good templates of optic disc and fovea shape and color for  pattern matching.

An automated fundus image analysis is used as a tool for the diagnosis of common retinal diseases. A good quality fundus image results in better diagnosis and hence discarding the degraded fundus images at the time of screening itself provides an opportunity to retake the adequate fundus photographs, which save both time and resources. In this paper, we propose a novel fundus image quality assessment (IQA) model using the convolutional neural network (CNN) based on the quality of optic disc (OD) visibility. We localize the OD by transfer learning with Inception v-3 model. Precise segmentation of OD is done using the GrabCut algorithm. Contour operations are applied to the segmented OD to approximate it to the nearest circle for finding its center and diameter. For training the model, we are using the publicly available fundus databases and a private hospital database. We have attained excellent classification accuracy for fundus IQA on DRIVE, CHASE-DB, and HRF databases. For the OD segmentation, we have experimented our method on DRINS-DB, DRISHTI-GS, and RIM-ONE v.3 databases and compared the results with existing state-of-the-art methods. Our proposed method outperforms existing methods for OD segmentation on Jaccard index and F-score metrics.

Optic disc detection and segmentation is one of the key elements for automatic retinal disease screening systems. The aim of this survey paper is to review, categorize and compare the optic disc detection algorithms and methodologies, giving a description of each of them, highlighting their key points and performance measures. Accordingly, this survey firstly overviews the anatomy of the eye fundus showing its main structural components along with their properties and functions. Consequently, the survey reviews the image enhancement techniques and also categorizes the image segmentation methodologies for the optic disc which include property-based methods, methods based on convergence of blood vessels, and model-based methods. The performance of segmentation algorithms is evaluated using a number of publicly available databases of retinal images via evaluation metrics which include accuracy and true positive rate (i.e. sensitivity). The survey, at the end, describes the different ab...

An automated fundus image analysis is used as a tool for the diagnosis of common retinal diseases. A good quality fundus image results in better diagnosis and hence discarding the degraded fundus images at the time of screening itself provides an opportunity to retake the adequate fundus photographs, which save both time and resources. In this paper, we propose a novel fundus image quality assessment (IQA) model using the convolutional neural network (CNN) based on the quality of optic disc (OD) visibility. We localize the OD by transfer learning with Inception v-3 model. Precise segmentation of OD is done using the GrabCut algorithm. Contour operations are applied to the segmented OD to approximate it to the nearest circle for finding its center and diameter. For training the model, we are using the publicly available fundus databases and a private hospital database. We have attained excellent classification accuracy for fundus IQA on DRIVE, CHASE-DB, and HRF databases. For the OD ...