Matrix Mask Overlapping and Convolution Eight Directions for Blood Vessel Segmentation on Fundus Retinal Image

Diabetic Retinopathy is one of the diseases that have the effect of a high mortality rate after heart disease and cancer.  However, the disease can be early detected through blood vessels and the optic nerve head in Fundus images. Blood vessels separation of the optic nerve head required high effort when it is conducted manually, therefore it is necessary that the appropriate method to perform segmentation of the object. Level Set method is well-known as object segmentation method based on object deformable. However, the methods have the disadvantage; it requires initialization before the segmentation process. In this research, segmentation method without initialization process is proposed. The segmentation is conducted by using the maximum value selection results of convolution 8 directions. Experimental results show that, proposed method has obtained 89.48% accuracy. Segmentation errors are caused by small branches, where they are not connected, so that the objects are supposed as noise

Review on Optic Disc Localization Techniques

The optic disc (OD) is one of the important part of the eye for detecting various diseases such as Diabetic Retinopathy and Glaucoma. The localization of optic disc is extremely important for determining hard exudates and lesions. Diagnosis of the disease can prevent people from vision loss. This paper analyzes various techniques which are proposed by different authors for the exact localization of optic disc to prevent vision loss

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

The Relationship of the Clinical Disc Margin and Bruch's Membrane Opening in Normal and Glaucoma Subjects.

PurposeWe tested the hypotheses that the mismatch between the clinical disc margin (CDM) and Bruch's membrane opening (BMO) is a function of BMO area (BMOA) and is affected by the presence of glaucoma.MethodsA total of 45 normal eyes (45 subjects) and 53 glaucomatous eyes (53 patients) were enrolled and underwent radial optic nerve head (ONH) imaging with spectral domain optical coherence tomography. The inner tip of the Bruch's membrane (BM) and the clinical disc margin were marked on radial scans and optic disc photographs, and were coregistered with custom software. The main outcome measure was the difference between the clinical disc area (CDA) and BMOA, or CDA-BMOA mismatch, as a function of BMOA and diagnosis. Multivariate regression analyses were used to explore the influence of glaucoma and BMOA on the mismatch.ResultsGlobal CDA was larger than BMOA in both groups but the difference was statistically significant only in the normal group (1.98 ± 0.37 vs. 1.85 ± 0.45 mm2, P = 0.02 in the normal group; 1.96 ± 0.38 vs. 1.89 ± 0.56 mm2, P = 0.08 in the glaucoma group). The sectoral CDA-BMOA mismatch was smaller in superotemporal (P = 0.04) and superonasal (P = 0.05) sectors in the glaucoma group. The normalized CDA-BMOA difference decreased with increasing BMOA in both groups (P < 0.001). Presence or severity of glaucoma did not affect the CDA-BMOA difference (P > 0.14).ConclusionsClinical disc area was larger than BMOA in normal and glaucoma eyes but reached statistical significance only in the former group. The CDA-BMOA mismatch diminished with increasing BMOA but was not affected by presence of glaucoma. These findings have important clinical implications regarding clinical evaluation of the ONH

Classification of visualization exudates fundus images results using support vector machine

This paper classifies the characteristics of normal and exudates fundus images by determine its accuracy for diagnostic purposes. Image normalization was performed on 149 fundus images (81 normal and 68 exudates) from MESSIDOR databases to standardize the colours in the fundus images. The OD removed fundus image and fundus image with the exudates areas removed. The SVM1 classifier was applied to 30 test fundus images to determine the best optimal parameter. The kernel function settings; linear, polynomial, quadratic and RBF have an effect on the classification results. For SVM1, the best parameter in classifying pixels is linear kernel function. The visualization results using CAC and radar chart are classified using ts accuracy. It has proven to discriminated exudates and non exudates pixels in fundus image using linear kernel function of SVM1 to diagnose DR.Keywords: Diabetic retinopathy (DR); Optic disc (OD); Support Vector Machine (SVM); AC); Radial Basis Function (RBF)

Intelligent optic disc segmentation using improved particle swarm optimization and evolving ensemble models

In this research, we propose Particle Swarm Optimization (PSO)-enhanced ensemble deep neural networks for optic disc (OD) segmentation using retinal images. An improved PSO algorithm with six search mechanisms to diversify the search process is introduced. It consists of an accelerated super-ellipse action, a refined super-ellipse operation, a modified PSO operation, a random leader-based search operation, an average leader-based search operation and a spherical random walk mechanism for swarm leader enhancement. Owing to the superior segmentation capabilities of Mask R-CNN, transfer learning with a PSO-based hyper-parameter identification method is employed to generate the fine-tuned segmenters for OD segmentation. Specifically, we optimize the learning parameters, which include the learning rate and momentum of the transfer learning process, using the proposed PSO algorithm. To overcome the bias of single networks, an ensemble segmentation model is constructed. It incorporates the results of distinctive base segmenters using a pixel-level majority voting mechanism to generate the final segmentation outcome. The proposed ensemble network is evaluated using the Messidor and Drions data sets and is found to significantly outperform other deep ensemble networks and hybrid ensemble clustering models that are incorporated with both the original and state-of-the-art PSO variants. Additionally, the proposed method statistically outperforms existing studies on OD segmentation and other search methods for solving diverse unimodal and multimodal benchmark optimization functions and the detection of Diabetic Macular Edema