Optic disc detection and segmentation using saliency mask in retinal fundus images

Accurate localization and segmentation of an optic disk (OD) is an important problem in the analysis of abnormality conditions such as optic disk shrinking/swelling, pale optic disk and glucoma. Hence, this paper proposes an automated fast and accurate OD localization and segmentation technique. In this work, OD localization is performed using the extended feature projection method (EFP) based on retinal vessel orientation and average intensity variance. Multiple OD candidate locations, obtained from OD localization technique (EFP), are used as the initialization points of the active contour model to detect the OD boundaries. Next, we use a decision tree based on the OD features such as the area of vessels, the brightness and the entropy to select the final OD candidate. The proposed technique has been tested on STARE dataset to evaluate comparative studies on the localization and segmentation of OD in retinal images. The accuracy of the OD localization is 90.12% with an average computing time of 13 seconds per image. The performance of the OD segmentation in terms of sensitivity is 74.62 % and positive predictive value is 60.22% with an average computing time of 20 seconds per image. The proposed approach improves the accuracy of conventional feature projection method by 12.34% and runs as quickly as the conventional one.

Accurate optic disc (OD) segmentation and fovea detection in retinal fundus images are crucial for diagnosis in ophthalmology. We propose a robust and broadly applicable algorithm for automated, robust, reliable and consistent fovea detection based on OD segmentation. The OD segmentation is performed with morphological operations and Fuzzy C Means Clustering combined with iterative thresholding on a foreground segmentation. The fovea detection is based on a vessel segmentation via morphological operations and uses the resulting OD segmentation to determine multiple regions of interest. The fovea is determined from the largest, vessel-free candidate region. We have tested the novel method on a total of 190 images from three publicly available databases DRIONS, Drive and HRF. Compared to results of two human experts for DRIONS database, our OD segmentation yielded a dice coefficient of 0.83. Note that missing ground truth and expert variability is an issue. The new scheme achieved an overall success rate of 99.44% for OD detection and an overall success rate of 96.25% for fovea detection, which is superior to state-of-the-art approaches.

Automatic optic disc (OD) localisation and segmentation is still a great challenge in computer‐aided diagnosis and screening system. Here, a new OD segmentation algorithm is proposed based on the distinct features of OD in terms of its intensity and shape. The algorithm includes four stages: image preprocessing, image segmentation, ellipse fitting, and OD localisation and segmentation. In the preprocessing stage, the blood vessel in the input retinal image is removed by using the morphological operation and median filtering in HSL (hue–saturation–lightness) colour space. In the image segmentation and ellipse fitting stages, the fractional‐order Darwinian particle swarm optimisation algorithm is used to extract the brightest region, and the least‐squares optimisation is adopted to detect elliptical OD shape. Finally, the smooth OD borders are generated in the last stage. The proposed method is evaluated by the centroid difference, overlapping ratio, overlap score, and success indexes. Experimental results on the retinal images from DRION, MESSIDOR, ORIGA, and many other public databases demonstrate that the proposed method has superior performance, and may be a suitable tool for automated retinal image analysis.

Efficient Optic Disc(OD) detection and segmentation is an essential element in the design of any automated retinal image analysis system. The detection of OD is often challenging due to the uneven illumination and lack of a definite OD boundary which is further complicated in infant retinal images due to their low quality. In this paper, a method for efficient localization and segmentation of OD in infant fundus images is proposed utilizing the commonly occurring double ring structure in the OD of premature infants. A method to assign an OD score to each pixel position is proposed which corresponds to the likelihood that the pixel belongs to the OD area, thus highlighting the possible locations of OD. The real location of OD is finalized after post-processing, and then the OD shape is segmented using a single seed region growing. The proposed method is extremely robust to the variations in the quality of the infant fundus images with 98.1% accuracy in locating the OD when tested on 317 infant fundus images of varying quality.

Aim: To evaluate a new algorithm to detect the optic disc in retinal fundus images on a number of publicly available datasets. Optic Disc detection is an important first step in many automated algorithms, either to be masked out of future processing or for the use in optic disc related disease such as glaucoma and papilledema.Methods: We propose a new method for optic disc detection that converts the retinal image into a graph and exploits vessel enhancement methods to calculate edge weights in finding the shortest path between pairs of points on the periphery of the image. The line segment with the maximum number of shortest paths is considered the optic disc location, with refinement from a combination template matching approach in the found region. The method was tested on three publicly available datasets: DRIVE, DIARETDB1, and Messidor consisting of 40, 89, and 1200 images. All images were acquired at a 45°-50° field of view.Results: The method achieves an accuracy of 100, 98.88, and 99.42% on the DRIVE, DIARETDB1, and Messidor databases respectively.Conclusions: The method performs as well or better than state-of-the-art methods on these datasets. Processing takes an average of 32 seconds (+-1.2) to detect the optic disc, with 26 of those seconds used for the vessel enhancement process. The accuracy over a wide variety of images shows that the method is robust and would be optimal for retinal analysis systems that perform vessel enhancement as part of their processing.

Regular eye examinations are the key to limiting the vision loss caused by glaucoma and diabetic retinopathy. Optic disc (OD) detection is of vital importance in developing automated diagnosis systems for these diseases. In this work we present a method for automatic localization and boundary detection of the optic disc in retinal fundus images. In the first step, we rely on the OD geometric feature and utilize gaussian and mean curvatures for the localization of the OD. In the second step, we extract a region of interest based on the OD localization. Then, to the edges of this extracted region a circular Hough transform is applied to segment the OD boundary. The experimental results on three public datasets show the efficacy of the proposed method.

Optic Disc (OD) localization is an important step in retinal image analysis especially for grading Diabetic retinopathy and Glaucoma. This paper describes a method to automatically localize the OD position in retinal fundus images. The method localizes the OD by combining intensity and vascular information. Initially, the vascular structure is extracted using 2 dimensional Gabor filtering followed by a simple thresholding technique. Then the initial OD candidates are selected using intensity information. The candidates’ satisfying area and density criterion are considered for the final stage. In the final stage, the entropy of vascular directions on the final candidates was calculated, and the candidate having maximum and optimum entropy was considered as OD region. The proposed method was evaluated on the two publicly available DRIVE and STARE databases. The method was able to obtain 100% of OD localization accuracy on DRIVE database with 2.2 seconds average computation time.

Human eye is the most sophisticated organ, with perfectly interrelated subsystems such as retina, pupil, iris cornea, lens and optic nerve. Uncontrolled diabetes retinopathy (DR) and glaucoma may lead to blindness. Optic disc helps to identify the different stages of DR, and glaucoma. In this paper, a novel automated, reliable and efficient optic disc localization and segmentation method using digital fundus images is proposed. General-purpose edge detection algorithms often fail to segment the optic disc (OD) due to fuzzy boundaries, inconsistent image contrast or missing edge features. This paper proposes a novel and probably the first method, using Attanassov intuitionistic fuzzy histon (A-IFSH) based segmentation to segment optic disc in retinal fundus images. Optic disc pixel intensity and column wise neighbourhood operation is employed to locate and isolate the optic disc. The method has been evaluated on 100 images comprising 30 normal, 39 glaucomatous and 31 DR images. Our proposed method yielded precision-0.93, recall-0.91, F-score-0.92 and mean segmentation accuracy of 93.4%. We have also compared the performance of our proposed method with Otsu and Gradient vector flow (GVF) snake methods. Overall, our result shows the superiority of proposed fuzzy segmentation technique over other two segmentation methods.

Glaucoma is a one of the serious diseases that occurs in retina. Early detection of glaucoma can prevent patients from blindness. One of the techniques to support the diagnosis of glaucoma is developed through the detection and segmentation of optic disc area. Optic disc area is also useful in assisting automated detection of abnormalities in the case of diabetic retinopathy. In this work, extracted red channel of colour retinal fundus images is used. Median filter is used to reduce noises in the red channel image. Segmentation of optic disc is conducted based on morphological operation. DRISHTI-GS dataset is used in this research works. Results indicate that the proposed method can achieve an accuracy of 94.546% in segmenting the optic disc.

Retinal images are extensively used for the disclosure of retinal vascular disorders like diabetic retinopathy, glaucoma, age-related macular degeneration and optic neuritis. The analysis of a diagnostic retinal image using a computer is necessary to ease the optometrist for automating the load screening mechanism to identify these disorders. The primary step in technology aided diagnoses is optic disc segmentation and it is considered as an interesting search problem. The proposed methodology involves pre-processing of fundus images and optic disc localisation using Jaya algorithm. A new fitness function is proposed in order to improve the accuracy of the optic disc localisation. The efficiency of the proposed methodology is examined on different datasets such as DIARETDB1, DIARETDB0, CHASEDB1, DRIONS and DRIVE. The results infer that the proposed methodology has achieved a 99% of location accuracy and shows its superiority in localising the optic disc by comparing it with existing literature.

Among the many diseases that affect the retina, there are two serious diseases: Diabetic Retinopathy and Glaucoma. Diabetic Retinopathy (DR) is a sight-threatening risk inflicting disorder diabetic patient. It occurs due to damage in the retina as a result of diabetes. Glaucoma is a disease of the retinal system that damages the optic nerve of the eye and gets worse over time. A buildup of pressure inside the eye is often associated with it, so it can damage the optic nerve that transmits images to the brain. If damage to the optic nerve caused by high eye pressure continues, glaucoma causes permanent vision loss. Early diagnosis and treatment have been shown to prevent blindness and visual loss. Compared with the manual diagnostic methods, automated retinal analysis systems help save patients' time, cost and vision. In this context, a fundamental process for diagnosing these diseases is the precise and effective localization of the Optic Disc (OD) in retinal images. This paper offers a robust and efficient method for automatic diagnosis of OD. The method starts with removing the undesirable portions of the image, such as noise, reflections and blur. Next, the OD has been detected by means of morphological operations considering the intensity threshold feature. The proposed method is fast and robustness to detect OD even if interrupted by the visible blood vessels. This method was applied on seven data sets which are the Origa, Rim-One 3, Drishti, Messidor, Drions, Diaretdb0 and DIARETDBI, and the resulted accuracy for these data set are 98.46, 97.48, 97.03, 98.75, 97.27, 95.38, 95.45 respectively.

Optic Disc (OD) detection in retinal fundus images is a crucial stage for the automation of a screening system in diabetic ophthalmology. Most researches for automatic localization of OD benefit the regions of vessels. In this paper, we present a fast and novel method based on the Circlet Transform to detect OD in digital retinal fundus images that doesn't utilize the location of the vessels. First, each R, G and B band is enhanced using CLAHE method. Then, the enhanced image in RGB color space is converted to L*a*b one. Next, the Circlet transform is applied to the L* band, and finally, the Circlet transform coefficients are analyzed to find the location of the OD. The proposed algorithm is implemented on DRIVE dataset and the experimental results show a very well OD localization. The correct rate of the proposed method is 95% even though it doesn't utilize the vessels' structure.

Diabetic retinopathy (DR) is a complication of retinal related common micro vascular disease occurred in diabetic patients. It is the one of the major causes of adult blindness in the worldwide. DR is indicated by the presence of hard exudates, soft exudates, haemorrhages, the growth of new blood vessels and foveal avascular zone (FAZ) enlargement. The objective of this work is to detect FAZ area in the colour retinal fundus images. In retinal fundus images, FAZ is located in the centre of macula, which is usually capillary-free. The detection of FAZ is an important step in the assessment of retinal images for visual related pathology detection. Retinal images are taken from Messidor database consisting of 59 retinal images. The proposed method has four main steps, i.e. pre-processing, blood vessels extraction, FAZ detection, and validation test. In the pre-processing step, morphological operation followed by contrast stretching is used to enhance and improve image quality. In other hand, matched filter and local entropy thresholding are applied to extract retinal blood vessels. FAZ area is detected by connecting retinal vessel endpoints. Finally, validation test is conducted by calculating the correlation between polygon area as detected FAZ by proposed method and circular area as labelled FAZ by ophthalmologists. The result achieves the correlation coefficient of 0.912 indicating that the proposed method has significant correlation with the method of the ophthalmologists.

Glaucoma is caused by damaged optic nerves, and can lead to permanent vision loss. The cup-to-disk ratio (CDR) is a key criterion for glaucoma diagnosis, therefore an accurate automatic segmentation of the optic disc (OD) and optic cup (OC) in retinal fundus images has become a major research topic. However, with deeper deep learning models being used to complete a fundus segmentation, the segmentation results remain acceptable only on certain datasets. In this research, a lightweight deep-learning encoder–decoder architecture, which adopts polar coordinate transformation and histogram equalization to simplify the learning complexity, is proposed for the simultaneous segmentation of OD and OC. The proposed model employs an encoder–decoder architecture consisting of a feature extraction module and a multi-output module to both simplify the model and to adjust the result through different outputs and multilabel loss functions. Experiment results demonstrate that the proposed approach outperforms other deep network models on OD and OC segmentation over the datasets REFUGE, MESSIDOR, and RIM-ONE, which contain images captured from cameras with various specifications. In addition, better results on glaucoma screening through CDR calculation were obtained on the REFUGE dataset with the proposed method.

The retinal images are widely used in the diagnosis and treatment of various eye diseases such as diabetic retinopathy, cataract and glaucoma. Normally retinal images are classified manually by clinicians trained in time and process resources. Segmentation imaging of the eye retina provides an important role in the calculation the shape and size of the optic disc and the anterior segment and the abnormal growth of any geometry in the eye region. It automatically and precisely calculates the values of location, position and contour area and a structural part of the image required by ophthalmologists. When there is an elevated intra ocular pressure from the normal condition, the subject is affected by glaucoma and in this condition the retinal nerve fiber layer and the optic disc are affected and this leads to progressive loss of vision. This paper proposes a novel method for glaucoma screening by using optic disc & cup segmentation. But , the segmentation methods suffer from many problems such as the optimization, and initialization inadequate in noisy images results. In this paper, we are tried to locate the most important region of the eye optic disc and optic cup using a morphological operations. The optic disc is the area on the retina optic nerve axons that enter and leave the eye. The proposed work consists pre processing using morphological operations and then segmentation of optic disc and optic cup by using the morphological operations. These operations are minimizing errors detection limit of the optic disc due to blood vessels cross. Then, the cup to disc ratio (CDR) is calculated using these operations. The cup to disc ratio is more than 0.3 then patients are glaucoma otherwise normal. Spatially Weighted Fuzzy C Mean (SWFCM) clustering method is used to segment the optic disc and Superpixel algorithm is used to segment the optic cup. The segmented optic disc and cup are then used to compute the CDR for glaucoma screening. Optic disc and cup segmentation of fundus image is considered for Diabetic Retinopathy Detection and Glucoma detection. The implemented results using MATLAB shows accurate results obtained both for diabetic retinopathy as well as glaucoma.