Automatic Detection Of Optic Disc Research Articles

A Dense sub-graph based approach for Automatic detection of Optic Disc

A Dense sub-graph based approach for Automatic detection of Optic Disc

A dense sub-graph-based approach for automatic detection of optic disc

A dense sub-graph-based approach for automatic detection of optic disc

Fully Convolutional Network and Visual Saliency-Based Automatic Optic Disc Detection in Retinal Fundus Images.

We present in this paper a novel optic disc detection method based on a fully convolutional network and visual saliency in retinal fundus images. Firstly, we employ the morphological reconstruction-based object detection method to locate the optic disc region roughly. According to the location result, a 400 × 400 image patch that covers the whole optic disc is obtained by cropping the original retinal fundus image. Secondly, the Simple Linear Iterative Cluster approach is utilized to segment such an image patch into many smaller superpixels. Thirdly, each superpixel is assigned a uniform initial saliency value according to the background prior information based on the assumption that the superpixels located on the boundary of the image belong to the background. Meanwhile, we use a pretrained fully convolutional network to extract the deep features from different layers of the network and design the strategy to represent each superpixel by the deep features. Finally, both the background prior information and the deep features are integrated into the single-layer cellular automata framework to gain the accurate optic disc detection result. We utilize the DRISHTI-GS dataset and RIM-ONE r3 dataset to evaluate the performance of our method. The experimental results demonstrate that the proposed method can overcome the influence of intensity inhomogeneity, weak contrast, and the complex surroundings of the optic disc effectively and has superior performance in terms of accuracy and robustness.

Automatic detection of optic disc using distance regularized level-set segmentation for glaucoma screening system

PurposeThe abnormalities of glaucoma have high impact on deciding and representing the causes that effects severity of blindness in human beings. The simulation experimental results would help the ophthalmologist in diagnosing of glaucoma abnormality accurately. The significant effect of glaucoma has a huge impact on the quality of human life, and its growth rate in world population tremendously increases. Glaucoma is considered as second largest cause for the blindness in the world; hence identification of it marks the importance of its detection at the earliest.Design/methodology/approachThe prime objective of the work proposed is to build up a human intervention free image preparing framework for glaucoma screening. The disc calculation is assessed on retinal image dataset called retinal Image for glaucoma Analysis. The proposed method briefs a novel optic disc division calculation depending on applying a level-set strategy on a confined optic disc image. In the instance of low quality image, a twofold level set is designed, in which the principal level set is viewed as restriction for the optic disc. To keep the veins from meddling with the level-set procedure, an inpainting strategy has been applied. Also a significant commitment is to include the varieties in notion adopted by the ophthalmologists in distinguishing the disc localization and diagnosing the glaucoma. Most of the past investigations are prepared and tested depending on just a single feature, which can be thought to be one-sided for the ophthalmologist.FindingsIn continuation, the correctness has been determined depending on the quantity of image that matched with the investigation pattern adopted by the ophthalmologist. The 175 retinal images were utilized to test the results of proposed work with the manual markings of ophthalmologists. The error-free calculation in marking the optic disc region and centroid was 98.95% in comparison with the existing result of 87.34%.Originality/valueIn continuation, the correctness has been determined depending on the quantity of image that matched with the investigation pattern adopted by the ophthalmologist. The 175 retinal images were utilized to test the results of proposed work with the manual markings of ophthalmologists. The error-free calculation in marking the optic disc region and centroid was 98.95% in comparison with the existing result of 87.34%.

A color fusion model based on Markowitz portfolio optimization for optic disc segmentation in retinal images

Retinal disorders are a severe health threat for older adults because they may lead to vision loss and blindness. Diabetic patients are particularly prone to suffer from Diabetic Retinopathy. Identifying relevant structural components in color fundus images like the optic disc (OD) is crucial to diagnose retinal diseases. Automatic OD detection is complex because of its location in an area where blood vessels converge, and color distribution is uneven. Several image processing techniques have been developed for OD detection so far, but vessel segmentation is sometimes required, increasing computational complexity and time. Moreover, precise OD segmentation methods utilize complex algorithms that need special hardware or extensive labeled datasets. We propose an OD detection approach based on the Modern Portfolio Theory of Markowitz to generate an innovative color fusion model. Specifically, the training phase calculates the optimal weights for each color channel. A fusion of weighted color channels is then applied in the testing phase. This approach acts as a powerful and real-time preprocessing stage. We use four heterogeneous datasets to validate the presented methodology. Three out of four datasets are publicly available (i.e., DRIVE, Messidor, and HRF), and the last corresponds to an in–house dataset acquired from Hospital Universitari Sant Joan de Reus (Spain). Two different segmentation methods are presented and compared with state-of-the-art computer vision techniques to analyze the model performance. An outstanding accuracy and overlap above 0.9 and 80%, respectively, and a minimal execution time of 0.05 s are reached. Therefore, our model could be integrated into daily clinical practice to accelerate the diagnosis of Diabetic Retinopathy due to its simplicity, performance, and speed.

Automatic Detection of Optic Disc for Diabetic Retinopathy

Diabetic Retinopathy affects the retina of the eye. Ultimately it may lead to total visual impairment. Total blindness can be avoided by detecting Diabetic Retinopathy at an early stage. Various manual tests are used by the doctors to detect the presence of disease but they are tedious and expensive. Some of the features of Diabetic Retinopathy are exudates, haemorrhages and micro aneurysms. Detection and removal of optic disc plays a vital role in extraction of these features. This paper focuses on detection of optic disc using various image processing techniques, algorithms such as Canny edge, Circular Hough(CHT). Retinal images from IDRiD, Diaret_db0, Diaret_db1, Chasedb and Messidor datasets were used.

Automatic optic disc detection in colour fundus images by means of multispectral analysis and information content.

The optic disc (OD) in retinal fundus images is widely used as a reference in computer-based systems for the measurement of the severity of retinal disease. A number of algorithms have been published in the past 5 years to locate and measure the OD in digital fundus images. Our proposed algorithm, automatically: (i) uses the three channels (RGB) of the digital colour image to locate the region of interest (ROI) where the OD lies, (ii) measures the Shannon information content per channel in the ROI, to decide which channel is most appropriate for searching for the OD centre using the circular Hough transform. A series of evaluations were undertaken to test our hypothesis that using the three channels gives a better performance than a single channel. Three different databases were used for evaluation purposes with a total of 2,371 colour images giving a misdetection error of 3% in the localisation of the centre of the OD. We find that the area determined by our algorithm which assumes that the OD is circular, is similar to that found by other algorithms that detected the shape of the OD. Five metrics were measured for comparison with other recent studies. Combining the two databases where expert delineation of the OD is available (1,240 images), the average results for our multispectral algorithm are: TPR = 0.879, FPR = 0.003, Accuracy = 0.994, Overlap = 80.6% and Dice index = 0.878.

Automatic optic disc detection using low-rank representation based semi-supervised extreme learning machine

Optic disc detection plays an important role in developing automatic screening systems for diabetic retinopathy. Several supervised learning-based approaches have been proposed for optic disc detection. However, these approaches demand that the input training examples are completely labelled. Essentially, in medical image analysis, it is difficult to prepare several training samples which were given reliable class labels due to the fact that manually labelling data is very expensive. Moreover, retinal images such as complex vessels structures in the optic disc constituting nonlinear relationships in high-dimensional observation space, which cannot work well by traditional linear classifiers. In this study, a novel approach named low-rank representation based semi-supervised extreme learning machine (LRR-SSELM) is proposed for automated optic disc detection. Our model has the following advantages. First, it detects the optic disc from the viewpoint of semi-supervised learning and overcomes the problem there are small portion of labelled samples. Second, a nonlinear classifier is introduced into our model to fully explore the nonlinear data. Third, the local and global structures of original data can be greatly persevered by low-rank representation (LRR). The performance of the proposed method is validated on three publicly available databases, DIARETDB0, DIARETDB1 and Messidor. The experimental results indicate the advantages and effectiveness of the proposed approach.

Automatic Optic Disc Detection in Color Retinal Images by Local Feature Spectrum Analysis.

The optic disc is a key anatomical structure in retinal images. The ability to detect optic discs in retinal images plays an important role in automated screening systems. Inspired by the fact that humans can find optic discs in retinal images by observing some local features, we propose a local feature spectrum analysis (LFSA) that eliminates the influence caused by the variable spatial positions of local features. In LFSA, a dictionary of local features is used to reconstruct new optic disc candidate images, and the utilization frequencies of every atom in the dictionary are considered as a type of “spectrum” that can be used for classification. We also employ the sparse dictionary selection approach to construct a compact and representative dictionary. Unlike previous approaches, LFSA does not require the segmentation of vessels, and its method of considering the varying information in the retinal images is both simple and robust, making it well-suited for automated screening systems. Experimental results on the largest publicly available dataset indicate the effectiveness of our proposed approach.

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.

Automatic Detection of Optic Disc and Blood Vessel in Retinal Images using Morphological Operations and Ipachi Model

Automated detection of Optic disc and blood vessel fabrication is becoming of virtual interest for better management of diabetic disease. In this paper, we propose morphology technique and new infinite active contour model that uses hybrid region information of image to approach this problem. For optic disc detection scheme utilizes operations such as edge detection, binary threshold and morphological operation. For vessel detection we use an infinite perimeter regularize, provided by using L2 Lebesgue measure of the boundaries, allows for better detection of small oscillatory structures than the models based on length of a feature’s boundaries. The advantage of using different types of region information, Eigen based enhancement, such as the combination of image intensity information and local phase based enhancement. The local phase based enhancement map is used for preserving vessel edges while the given intensity information will give a correct feature’s segmentation.

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.

Automatic optic disc detection in OCT slices via low-rank reconstruction.

Optic disc measurements provide useful diagnostic information as they have correlations with certain eye diseases. In this paper, we provide an automatic method for detecting the optic disc in a single OCT slice. Our method is developed from the observation that the retinal pigment epithelium (RPE) which bounds the optic disc has a low-rank appearance structure that differs from areas within the disc. To detect the disc, our method acquires from the OCT image an RPE appearance model that is specific to the individual and imaging conditions, by learning a low-rank dictionary from image areas known to be part of the RPE according to priors on ocular anatomy. The edge of the RPE, where the optic disc is located, is then found by traversing the retinal layer containing the RPE, reconstructing local appearance with the low-rank model, and detecting the point at which appearance starts to deviate (i.e., increased reconstruction error). To aid in this detection, we also introduce a geometrical constraint called the distance bias that accounts for the smooth shape of the RPE. Experiments demonstrate that our method outperforms other OCT techniques in localizing the optic disc and estimating disc width. Moreover, we also show the potential usage of our method on optic disc area detection in 3-D OCT volumes.

Automatic Detection of Optic Disc in Digital Retinal Images

On the research work leading to automatic detection of optic disc from retinal images is very essential and crucial for expert ophthalmologists to diagnose diseases. Many of techniques can achieve good performance on retinal feature that is clearly visible. Unfortunately, it is a normal situation that the color retinal images in Thailand are poor-quality images. The existing algorithm cannot detected poor-quality images. Therefore, this study is a part of larger efforts to develop a novel method for detection of optic disc in poorquality retinal images. A novel method is presented towards the development for detection of optic disc in poor-quality retinal images. The digital retinal images are detected by using morphological method and Otsu’s algorithm after the key preprocessing steps, i.e., color normalization, contrast enhancement and noise removal. This enables the difference in the proposed method compared to other approaches and the algorithm can achieve good performance even on poor-quality retinal images. The proposed method was evaluated using the local dataset and the publicly available of the STARE project’s dataset. The optic disc was detected correctly in 91.35% using the STARE dataset and 97.61% using the local dataset. This system intends to help expert ophthalmologists in screening process to detect of optic disc faster and more easily.

GA based Automatic Optic Disc Detection from Fundus Image using Blue Channel and Green Channel Information

Proper detection of Optic disc (OD) in a fundus image is an important stage in an automated method to screen diabetic retinopathy. To identify the OD the intensity of the pixel is used. The pixel of the OD is different from the rest of the fundus image and intensity based techniques such as thresholding may be applied for the detection. Due to more brighter region false resemblens of OD is generated and the intensity based technique fails to detect the OD properly. Using techniques like template matching the OD is detected [1] and [2]. The sliding window tehnique applied in template matching is time consuming. The method proposed is using Genetic Algorithm which will search the entire fundus image in a short time. Almost in every intensity based method, the red channel and green channel informations are used. The Blue channel information is neglated and the reason for avoiding is no potential information in the blue channel, which is not true. Since we are detecting the OD and not the Exudates or other symptoms the blue channel will be suitable for a dull image.In this work, we tried to use the blue layer information as the major clue for the successful detection of optic disc location and size in a typical fundus image. We designed a genetic algorithm based algorithm as well as a direct search based algorithm to locate the exact position and size of the OD using blue layer and green layer intensity in a suitable fitness function.

Automatic Detection of Optic Disc Based on PCA and Mathematical Morphology

The algorithm proposed in this paper allows to automatically segment the optic disc from a fundus image. The goal is to facilitate the early detection of certain pathologies and to fully automate the process so as to avoid specialist intervention. The method proposed for the extraction of the optic disc contour is mainly based on mathematical morphology along with principal component analysis (PCA). It makes use of different operations such as generalized distance function (GDF), a variant of the watershed transformation, the stochastic watershed, and geodesic transformations. The input of the segmentation method is obtained through PCA. The purpose of using PCA is to achieve the grey-scale image that better represents the original RGB image. The implemented algorithm has been validated on five public databases obtaining promising results. The average values obtained (a Jaccard's and Dice's coefficients of 0.8200 and 0.8932, respectively, an accuracy of 0.9947, and a true positive and false positive fractions of 0.9275 and 0.0036) demonstrate that this method is a robust tool for the automatic segmentation of the optic disc. Moreover, it is fairly reliable since it works properly on databases with a large degree of variability and improves the results of other state-of-the-art methods.

Combining algorithms for automatic detection of optic disc and macula in fundus images

This paper proposes an efficient combination of algorithms for the automated localization of the optic disc and macula in retinal fundus images. There is in fact no reason to assume that a single algorithm would be optimal. An ensemble of algorithms based on different principles can be more accurate than any of its individual members if the individual algorithms are doing better than random guessing. We aim to obtain an improved optic disc and macula detector by combining the prediction of multiple algorithms, benefiting from their strength and compensating their weaknesses. The location with maximum number of detectors’ outputs is formally the hotspot and is used to find the optic disc or macula center. An assessment of the performance of integrated system and detectors working separately is also presented. Our proposed combination of detectors achieved overall highest performance in detecting optic disc and fovea closest to the manually center chosen by the retinal specialist.

Automatic Optic Disc Detection From Retinal Images by a Line Operator

Under the framework of computer-aided eye disease diagnosis, this paper presents an automatic optic disc (OD) detection technique. The proposed technique makes use of the unique circular brightness structure associated with the OD, i.e., 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. A line operator is designed to capture such circular brightness structure, which evaluates the image brightness variation along multiple line segments of specific orientations that pass through each retinal image pixel. The orientation of the line segment with the minimum/maximum variation has specific pattern that can be used to locate the OD accurately. The proposed technique has been tested over four public datasets that include 130, 89, 40, and 81 images of healthy and pathological retinas, respectively. Experiments show that the designed line operator is tolerant to different types of retinal lesion and imaging artifacts, and an average OD detection accuracy of 97.4% is obtained.

Automated Diagnosis of Glaucoma Using Digital Fundus Images

Glaucoma is a disease of the optic nerve caused by the increase in the intraocular pressure of the eye. Glaucoma mainly affects the optic disc by increasing the cup size. It can lead to the blindness if it is not detected and treated in proper time. The detection of glaucoma through Optical Coherence Tomography (OCT) and Heidelberg Retinal Tomography (HRT) is very expensive. This paper presents a novel method for glaucoma detection using digital fundus images. Digital image processing techniques, such as preprocessing, morphological operations and thresholding, are widely used for the automatic detection of optic disc, blood vessels and computation of the features. We have extracted features such as cup to disc (c/d) ratio, ratio of the distance between optic disc center and optic nerve head to diameter of the optic disc, and the ratio of blood vessels area in inferior-superior side to area of blood vessel in the nasal-temporal side. These features are validated by classifying the normal and glaucoma images using neural network classifier. The results presented in this paper indicate that the features are clinically significant in the detection of glaucoma. Our system is able to classify the glaucoma automatically with a sensitivity and specificity of 100% and 80% respectively.