Disc In Retinal Images Research Articles

Segmentation of optic disc in retinal images for glaucoma diagnosis by saliency level set with enhanced active contour model

<p>Glaucoma is an ophthalmic disease which is among the chief causes of visual impairment across the globe. The clarity of the optic disc (OD) is crucial for recognizing glaucoma. Since existing methods are unable to successfully integrate multi-view information derived from shape and appearance to precisely explain OD for segmentation, this paper proposes a saliency-based level set with an enhanced active contour method (SL-EACM), a modified locally statistical active contour model, and entropy-based optical disc localization. The significant contributions are that i) the SL-EACM is introduced to address the often noticed problem of intensity inhomogeneity brought on by defects in imaging equipment or fluctuations in lighting; ii) to prevent the integrity of the OD structures from being compromised by pathological alterations and artery blockage, local image probability data is included from a multi-dimensional feature space around the region of interest in the model; and iii) the model incorporates prior shape information into the technique, for enhancing the accuracy in identifying the OD structures from surrounding regions. Public databases such as CHASE_DB, DRIONS-DB, and Drishti-GS are used to evaluate the proposed model. The findings from numerous trials demonstrate that the proposed model outperforms state-of-the-art approaches in terms of qualitative and quantitative outcomes.</p>

Frequency domain analysis of grey level intensities for extraction of optic disc in retinal images

Revealing and extraction of optic disc (OD) in fundus images is the most important step in automatic screening system of diabetic retinopathy. The algorithm presented in this paper is focused on revealing and extraction of optic disc (OD) in fundus images. This algorithm adopts frequency domain approach to focus the OD and mathematical morphology for extraction of the OD. Large pathological signs, bright lesions limit the OD segmentation performance as brightness of both OD and bright lesions is similar. Bright lesions are extracted before localisation of the OD using DFT and morphological dilation is used for segmentation of the OD. This method was assessed on standard databases namely: DRIVE, DIARETDB1 and DIARETDB0. This algorithm acceptably recognises the OD in 255 out of 259 retinal images (98.45%) in 0.75 seconds.78.9-86.65% and 99.37-99.58% are the OD segmentation sensitivity and specificity achieved on these three databases. The proposed method demonstrates acceptable robustness on normal and pathological signs retinal images. Focused experimental results reveal the superior performance of presented work with respect to the methods demonstrated in literature.

Frequency domain analysis of grey level intensities for extraction of optic disc in retinal images

Revealing and extraction of optic disc (OD) in fundus images is the most important step in automatic screening system of diabetic retinopathy. The algorithm presented in this paper is focused on revealing and extraction of optic disc (OD) in fundus images. This algorithm adopts frequency domain approach to focus the OD and mathematical morphology for extraction of the OD. Large pathological signs, bright lesions limit the OD segmentation performance as brightness of both OD and bright lesions is similar. Bright lesions are extracted before localisation of the OD using DFT and morphological dilation is used for segmentation of the OD. This method was assessed on standard databases namely: DRIVE, DIARETDB1 and DIARETDB0. This algorithm acceptably recognises the OD in 255 out of 259 retinal images (98.45%) in 0.75 seconds.78.9-86.65% and 99.37-99.58% are the OD segmentation sensitivity and specificity achieved on these three databases. The proposed method demonstrates acceptable robustness on normal and pathological signs retinal images. Focused experimental results reveal the superior performance of presented work with respect to the methods demonstrated in literature.

Multi-atlas segmentation of optic disc in retinal images via convolutional neural network

Multi-atlas segmentation is widely accepted as an essential image segmentation approach. Through leveraging on the information from the atlases instead of utilizing the model-based segmentation techniques, the multi-atlas segmentation could significantly enhance the accuracy of segmentation. However, label fusion, which plays an important role for multi-atlas segmentation still remains the primary challenge. Bearing this in mind, a deep learning-based approach is presented through integrating feature extraction and label fusion. The proposed deep learning architecture consists of two independent channels composing of continuous convolutional layers. To evaluate the performance our approach, we conducted comparison experiments between state-of-the-art techniques and the proposed approach on publicly available datasets. Experimental results demonstrate that the accuracy of the proposed approach outperforms state-of-the-art techniques both in efficiency and effectiveness.

Saliency‐Based Segmentation of Optic Disc in Retinal Images

Accurate segmentation of Optic disc (OD) is significant for the automation of retinal analysis and retinal diseases screening. This paper proposes a novel optic disc segmentation method based on the saliency. It includes two stages: optic disc location and saliency-based segmentation. In the location stage, the OD is detected using a matched template and the density of the vessels. In the segmentation stage, we treat the OD as the salient object and formulate it as a saliency detection problem. To measure the saliency of a region, the boundary prior and the connectivity prior are exploited. Geodesic distance to the window boundary is computed to measure the cost the region spends to reach the window boundary. After a threshold and ellipse fitting, we obtain the OD. Experimental results on two public databases for OD segmentation show that the proposed method achieves thestate-of-the-art performance.

Detection of Optic Disc Centre Point in Retinal Image

Glaucoma and diabetic retinopathy (DR) are the two most common retinal related diseases occurred in the world. Glaucoma can be diagnosed by measuring optic cup to disc ratio (CDR) defined as optic cup to optic disc vertical diameter ratio of retinal fundus image. A computer based optic disc is expected to assist the ophthalmologist to find their location which are necessary for glaucoma and DR diagnosis. However, many optic disc detection algorithms available now are commonly non-automatic and only work in healthy retinal image. Therefore, there is not information on how optic disc in retinal image of unhealthy patient can be extracted computationally. In this research work, the method for automated detection of optic disc on retinal colour fundus images has been developed to facilitate and assist ophthalmologists in the diagnosis of retinal related diseases. The results indicated that the proposed method can be implemented in computer aided diagnosis of glaucoma and diabetic retinopathy system development.

Localizing Optic Disc in Retinal Image Automatically with Entropy Based Algorithm

Examining retinal image continuously plays an important role in determining human eye health; with any variation present in this image, it may be resulting from some disease. Therefore, there is a need for computer-aided scanning for retinal image to perform this task automatically and accurately. The fundamental step in this task is identification of the retina elements; optical disk localization is the most important one in this identification. Different optical disc localization algorithms have been suggested, such as an algorithm that would be proposed in this paper. The assumption is based on the fact that optical disc area has rich information, so its entropy value is more significant in this area. The suggested algorithm has recursive steps for testing the entropy of different patches in image; sliding window technique is used to get these patches in a specific way. The results of practical work were obtained using different common data set, which achieved good accuracy in trivial computation time. Finally, this paper consists of four sections: a section for introduction containing the related works, a section for methodology and material, a section for practical work with results, and a section for conclusion.

DETECTION OF DIABETIC RETINOPATHY USING OPTIC DISC

This paper proposes a method for the automatic detection of optic disc in retinal images. In the diagnosis and grading, the essential step is recognition of optic disk for diabetic retinopathy. The analysis of directional cross section profile focused on the local maximum pixel of pre-processed image is realized by the proposed method using optic disc detection. Each profile is implemented by peak detection and property like shape, size and height of the peak are estimated. The statistical measure of the estimated values for the attributes, where the orientation of the cross-section changes the constitute feature used in morphological classification to exclude encourages candidates. The result is to find the patient is affected by diabetics or not.Keywords: Diabetic retinopathy, Optic disk, Naives Bayes algorithm, Local maximum region.

An Automatic Segmentation & Detection of Blood Vessels and Optic Disc in Retinal Images

—Conceptual Segmentation is a critical technique in medical imaging. The Processes of identification and division of optic circle and veins are the fundamental strides for the analysis of a few infections that causes visual deficiency like diabetic retinopathy, hypertension, glaucoma and different visual deficiency ailment.

Speedy Location of Optic Disc in Retinal Images

Speedy Location of Optic Disc in Retinal Images

Automatic segmentation of optic disk in retinal images

Abstract In this paper a new algorithm is presented for automatic segmentation of the optic disk (OD) in human retinal images. In proposed method, an equiripple low pass finite impulse response (FIR) filter is designed to suppress the dominance of blood vessels and to enhance the OD region in retinal image. Desired frequency response with optimized order of the filter is achieved by choosing proper values of filter design parameters. This method computes the appropriate value of threshold to locate the OD in retinal image. Segmentation of the OD region is carried out using grayscale morphological dilation and median filtering operation. The proposed algorithm has been tested on four standard databases (DRIVE, DIRATEDB0, DIRATEDB1 and DRIONS database). The proposed algorithm detects the OD successfully with accuracies 100%, 96.92%, 98.98%, 100% on DRIVE, DIRATEDB0, DIRATEDB1 and DRIONS databases respectively. The OD segmentation sensitivity and specificity are in the range of 74.60–87.07%, 99.39–99.61% on these four databases. The proposed algorithm has low computation complexity as it takes low computation time to segment the OD over existing methods. Also it is robust to differences in image illumination and retinal anomalies. Hence the proposed method can be used for automatic screening system of retinal related diseases like glaucoma and diabetic retinopathy.

Localization and segmentation of optic disc in retinal images using circular Hough transform and grow-cut algorithm.

Automated retinal image analysis has been emerging as an important diagnostic tool for early detection of eye-related diseases such as glaucoma and diabetic retinopathy. In this paper, we have presented a robust methodology for optic disc detection and boundary segmentation, which can be seen as the preliminary step in the development of a computer-assisted diagnostic system for glaucoma in retinal images. The proposed method is based on morphological operations, the circular Hough transform and the grow-cut algorithm. The morphological operators are used to enhance the optic disc and remove the retinal vasculature and other pathologies. The optic disc center is approximated using the circular Hough transform, and the grow-cut algorithm is employed to precisely segment the optic disc boundary. The method is quantitatively evaluated on five publicly available retinal image databases DRIVE, DIARETDB1, CHASE_DB1, DRIONS-DB, Messidor and one local Shifa Hospital Database. The method achieves an optic disc detection success rate of 100% for these databases with the exception of 99.09% and 99.25% for the DRIONS-DB, Messidor, and ONHSD databases, respectively. The optic disc boundary detection achieved an average spatial overlap of 78.6%, 85.12%, 83.23%, 85.1%, 87.93%, 80.1%, and 86.1%, respectively, for these databases. This unique method has shown significant improvement over existing methods in terms of detection and boundary extraction of the optic disc.

Extraction of blood vessels and optic disc in retinal images

Blood vessels and optic disc (OD) detection is important in developing systems for automated diagnosis of various ophthalmic pathologies. This work proposes two efficient approaches for automatic detection of blood vessels and OD in retinal images. For extraction of blood vessels, first the Gabor filters are applied so as to enhance the contrast between the blood vessels and the background, and then, local entropy thresholding is applied for segmentation. The DRIVE database is used for comparative analysis. The average accuracy, sensitivity and specificity of proposed method are 93.85, 70.99 and 97.35%, respectively. The proposed technique produced better segmentation when compared with the existing algorithms. For localisation and detection of OD, a new histon-based algorithm is proposed in which roughness index of the histon and histogram is used for thresholding. The localised OD area is used as a mask, since it reduces execution time for segmentation of the OD. The proposed techniques have been tested over publicly available data-sets. The OD localisation accuracy for DRIVE and DIARETDB1 database is 95 and 91%, respectively.

Automatic Detection of Optic Disc in Retinal Image by Using Keypoint Detection, Texture Analysis, and Visual Dictionary Techniques.

With the advances in the computer field, methods and techniques in automatic image processing and analysis provide the opportunity to detect automatically the change and degeneration in retinal images. Localization of the optic disc is extremely important for determining the hard exudate lesions or neovascularization, which is the later phase of diabetic retinopathy, in computer aided eye disease diagnosis systems. Whereas optic disc detection is fairly an easy process in normal retinal images, detecting this region in the retinal image which is diabetic retinopathy disease may be difficult. Sometimes information related to optic disc and hard exudate information may be the same in terms of machine learning. We presented a novel approach for efficient and accurate localization of optic disc in retinal images having noise and other lesions. This approach is comprised of five main steps which are image processing, keypoint extraction, texture analysis, visual dictionary, and classifier techniques. We tested our proposed technique on 3 public datasets and obtained quantitative results. Experimental results show that an average optic disc detection accuracy of 94.38%, 95.00%, and 90.00% is achieved, respectively, on the following public datasets: DIARETDB1, DRIVE, and ROC.

Improved automated vessel segmentation for diagnosing eye diseases using fundus images

The retina can provide evidence of diseases originating in other parts of the body. Among the eye diseases that can be diagnosed through a retinal examination, age-related macular degeneration, glaucoma, and diabetic retinopathy are the most common, and they cause vision loss. Although these diseases can be diagnosed by examining blood vessels and the optic disk in retinal images, assessment of blood vessels on colored fundus images is a time-consuming and subjective process. Here, we present an automated blood vessel segmentation algorithm that facilitates the evaluation of diabetic retinopathy through assessment of blood vessel abnormalities. The blood vessels are extracted using a random forest classification model combined with wavelet features and local binary pattern texture information. Discriminant analysis is modified and used for feature selection to train the proposed classification model. The boundary of the optic disk is identified using low-pass filtering, fuzzy c-means clustering, and template matching so that it may be removed and not confound the segmentation analysis. Validation test results using three publicly available retinal image datasets demonstrated that our proposed method achieves as good or better blood vessel segmentation accuracy than the other supervised model approaches examined. Results show that the proposed scheme is able to segment the blood vessels and optic disk structures accurately (Accuracy Index) in 95.80%, 95.20%, and 97.10% of the testing Digital Retinal Images for Vessel Extraction (DRIVE), Structured Analysis of the Retina (STARE), and CHASE_DB1 image datasets respectively. The main advantage of our proposed model is that it provides robust and computationally efficient segmentation of blood vessels and the optic disk. The proposed model aims to provide supportive information for cases in which a diagnosis remains unclear following a clinical examination.

An approach to locate optic disc in retinal images with pathological changes

Automatic optic disc (OD) detection is an essential step for screening of eye diseases. An OD localization method is proposed in this paper, which aims to locate OD robustly in retinal image with pathological changes. There are mainly three steps in this approach: region-of-interest (ROI) detection, candidate pixel detection, and confidence score calculation. The features of vessel direction, intensity, OD edges, and size of bright regions were extracted and employed in the proposed OD locating approach. Compared with the OD locating method based on vessel direction only, the proposed method could handle the following cases better: OD partially appears in retinal image, retinal vessels are not obvious in retinal image, or there are bright lesions in retinal images. Four public databases with total 340 retinal images were tested to evaluate the performance of our method. The proposed method can achieve an accuracy of 100%, 95.8%, 99.2%, 97.8% for DRIVE database, STARE database, DIARETDB0 database, DIARETDB1 database respectively. Comparison studies showed that the proposed approach is especially robust in the retinal images with diseases.

Computer Aided Screening of Optic Disc in Retinal Images Using Binary Orientation Map

Computer Aided Screening of Optic Disc in Retinal Images Using Binary Orientation Map

Effective Segmentation of Optic Disc in Retinal images for diagnosing Eye Diseases that leads to Blindness

Human Eye is one of the most sophisticated organ, with cornea ,retinal pigment epithelium, bruch membrane, macula, vitreous body, retina and optic nerve. Retinal image analysis is broadly used for screening the patients affected from sight threatening eye diseases like Diabetic Retinopathy (DR) and glaucoma. Optic disc (OD) margin is the mandatory landmark in establishing a frame of reference of classifying retinal and optic nerve pathology. Trustworthy and efficient OD localization and segmentation are important tasks in automatic eye disease screening. This paper presents a fully automated method for OD segmentation algorithm developed for retinal disease screening. First, preprocessing step used to remove noise by illumination correction and contrast enhancement. Second, a thresholding technique using Atanassov’s intuitionistic fuzzy set (A-IFS) is employed. This approach uses A-IFS histon, an encrustation of the histogram consist of the pixels that belong to the set of similar pixels, in roughness index A-IFS histon & histogram correlated to upper and lower approximations and optimum threshold value is identified which pixel belong to background or to object(OD). This approach is evaluated by means of three publicly available databases DRIVE, MESSIDOR, DIARETDB0, the experimental outcome shows that the overall performance is with 99% correct optic disc localization.

Segmentation of Blood Vessels and Optic disc in Retinal Images

Understanding of picture substance is one of the targets in PC vision particularly in picture transforming. In this period it has gotten much familiarity with analysts. In picture elucidation the segment of the picture into article and foundation is a serious step. Division differentiates a picture into its segment locales or articles. Picture division t needs to portion the article from the foundation to peruse the picture legitimately and recognize the substance of the picture painstakingly. In this setting, edge identification is a principal apparatus for picture division. In this paper an endeavor is made to study the execution of most usually utilized edge location strategies for picture division.

Segmentation and Classification of Optic Disc in Retinal Images

Image segmentation plays a vital role in image analysis for diagnosis of various retinopathy diseases. For the detection of glaucoma and diabetic retinopathy, manual examination of the optic disc is the standard clinical procedure. The proposed method makes use of the circular transform to automatically locate and extract the Optic Disc (OD) from the retinal fundus images. The circular transform operates with radial line operator which uses the multiple radial line segments on every pixel of the image. The maximum variation pixels along each radial line segments are taken to detect and segment OD. The input retinal images are preprocessed before applying circular transform. The optic disc diameter and the distance from optic disc to macula are found for a sample of 20 images. An Extreme Learning Machine classifier is used to train the neural network to classify the images as normal or abnormal. Its performance is compared with Support Vector Machine in terms of computation time and accuracy. It is found that computation time is less than 0.1 sec and accuracy is 97.14% for Extreme Learning Machine classifier.