Segmentation of the optic disc, macula and vascular arch in fundus photographs

Which Color Channel Is Better for Diagnosing Retinal Diseases Automatically in Color Fundus Photographs?

Color fundus photographs are the most common type of image used for automatic diagnosis of retinal diseases and abnormalities. As all color photographs, these images contain information about three primary colors, i.e., red, green, and blue, in three separate color channels. This work aims to understand the impact of each channel in the automatic diagnosis of retinal diseases and abnormalities. To this end, the existing works are surveyed extensively to explore which color channel is used most commonly for automatically detecting four leading causes of blindness and one retinal abnormality along with segmenting three retinal landmarks. From this survey, it is clear that all channels together are typically used for neural network-based systems, whereas for non-neural network-based systems, the green channel is most commonly used. However, from the previous works, no conclusion can be drawn regarding the importance of the different channels. Therefore, systematic experiments are conducted to analyse this. A well-known U-shaped deep neural network (U-Net) is used to investigate which color channel is best for segmenting one retinal abnormality and three retinal landmarks.

A robust method for the automatic location of the optic disc and the fovea in fundus images

BACKGROUND AND OBJECTIVE

The location of the optic disc (OD) and the fovea is usually crucial in automatic screening systems for diabetic retinopathy. Previous methods aimed at their location often fail when these structures do not have the standard appearance. The purpose of this work is to propose novel, robust methods for the automatic detection of the OD and the fovea.

METHODS

The proposed method comprises a preprocessing stage, a method for retinal background extraction, a vasculature segmentation phase and the computation of various novel saliency maps. The main novelty of this work is the combination of the proposed saliency maps, which represent the spatial relationships between some structures of the retina and the visual appearance of the OD and fovea. Another contribution is the method to extract the retinal background, based on region-growing.

RESULTS

The proposed methods were evaluated over a proprietary database and three public databases: DRIVE, DiaretDB1 and Messidor. For the OD, we achieved 100% accuracy for all databases except Messidor (99.50%). As for the fovea location, we also reached 100% accuracy for all databases except Messidor (99.67%).

CONCLUSIONS

Our results suggest that the proposed methods are robust and effective to automatically detect the OD and the fovea. This way, they can be useful in automatic screening systems for diabetic retinopathy as well as other retinal diseases.

Robust Fovea Localization Based on Symmetry Measure

Automatic fovea localization is a challenging issue. In this article, we focus on the study of fovea localization and propose a robust fovea localization method. We propose concentric circular sectional symmetry measure (CCSSM) for symmetry axis detection, and region of interest (ROI) determination, which is a global feature descriptor robust against local feature changes, to solve the lesion interference issue, i.e., fovea visibility interference from lesions, using both structure features and morphological features. We propose the index of convexity and concavity (ICC) as the convexity-concavity measure of the surface and provide a quantitative evaluation tool for ophthalmologists to learn whether the occurrence of lesion within the ROI. We propose the weighted gradient accumulation map, which is insensitive to local intensity changes and can overcome the influence of noise and contamination, to perform refined localization. The advantages of the proposed method lies in two aspects. First, the accuracy and robustness can be achieved without typical sophisticated manner, i.e., blood vessel segmentation and parabola fitting. Second, the lesion interference is considered in our plan of fovea localization. Our proposed symmetry-based method is innovative in the solution of fovea detection, and it is simple, practical, and controllable. Experiment results show that the proposed method can resist the interference of unbalanced illumination and lesions, and achieve high accuracy rate in five datasets. Compared to the state-of-the-art methods, high robustness and accuracy of the proposed method guarantees its reliability.

Optical coherence tomography angiography for the measurement of optic disc: Macular relationship

INTRODUCTION

Fundus photography is the gold standard for assessing ocular torsion over the last 30 years. However, it is not a precise and reproducible tool during clinical practice. Optical coherence tomography angiography is characterized by precise identification of the macula and the optic disc, and it could be an effective method to easily calculate the angle of ocular torsion, compared to fundus photography. The aim of this study was to show whether any difference in the measurement and the accuracy of the angle of torsion between the head of the optic nerve and the fovea was present.

METHODS

This is a prospective single-, referral-center study conducted at the San Giuseppe Hospital in Milan on 80 eyes of 40 adult patients, included in a random-sample way. Exclusion criteria were non-cooperation, higher refractive errors of ±3 diopters, retinal and optic disc pathologies, and ocular movement disorders.

RESULTS

Patients' mean age was 54.3 ± 16.3 (range: 22-83) years. The angle measured by the fundus camera was 7.78° ± 3.04°, while the angle measured by the angiography was 7.09° ± 3.08° (p = .035). The mean interocular difference was 1.54° ± 3.42° for fundus photography and 0.5° ± 4.71° for angiography (p = .013).

CONCLUSION

Optical coherence tomography angiography is a very useful, fast, precise, reproducible, and reliable technique in cooperative subjects, not inferior to the fundus camera and less prone to human error.

Automatic Screening of Diabetic Maculopathy Using Image Processing

Retinal imaging is a challenging screening method for detection of retinal abnormalities. Diabetic Maculopathy (DM) is a condition that can result from retinopathy. Regular screening is necessary for diabetic maculopathy in order to identify the risk of vision loss. Maculopathy is damage to macula, the key region responsible for high sharp colour vision. Diabetic Retinopathy and Diabetic Maculopathy needs regular observation in order to indicate visual impairment risk. In this article, the author first presents a brief summary of diabetic maculopathy and its causes. Then, an exhaustive literature review of different automated DM diagnosis systems offered. It is important for ophthalmologists to have an automated system which detects early symptoms of the disease and yields a high accurate result. A vital assessment of the image processing techniques used for DM feature detection is projected in this paper. Various methods have been proposed to identify and classify DM based on severity level.

A review on computer-aided recent developments for automatic detection of diabetic retinopathy

Diabetic retinopathy is a serious microvascular disorder that might result in loss of vision and blindness. It seriously damages the retinal blood vessels and reduces the light-sensitive inner layer of the eye. Due to the manual inspection of retinal fundus images on diabetic retinopathy to detect the morphological abnormalities in Microaneurysms (MAs), Exudates (EXs), Haemorrhages (HMs), and Inter retinal microvascular abnormalities (IRMA) is very difficult and time consuming process. In order to avoid this, the regular follow-up screening process, and early automatic Diabetic Retinopathy detection are necessary. This paper discusses various methods of analysing automatic retinopathy detection and classification of different grading based on the severity levels. In addition, retinal blood vessel detection techniques are also discussed for the ultimate detection and diagnostic procedure of proliferative diabetic retinopathy. Furthermore, the paper elaborately discussed the systematic review accessed by authors on various publicly available databases collected from different medical sources. In the survey, meta-analysis of several methods for diabetic feature extraction, segmentation and various types of classifiers have been used to evaluate the system performance metrics for the diagnosis of DR. This survey will be helpful for the technical persons and researchers who want to focus on enhancing the diagnosis of a system that would be more powerful in real life.

Combination of Global Features for the Automatic Quality Assessment of Retinal Images

Diabetic retinopathy (DR) is one of the most common causes of visual loss in developed countries. Computer-aided diagnosis systems aimed at detecting DR can reduce the workload of ophthalmologists in screening programs. Nevertheless, a large number of retinal images cannot be analyzed by physicians and automatic methods due to poor quality. Automatic retinal image quality assessment (RIQA) is needed before image analysis. The purpose of this study was to combine novel generic quality features to develop a RIQA method. Several features were calculated from retinal images to achieve this goal. Features derived from the spatial and spectral entropy-based quality (SSEQ) and the natural images quality evaluator (NIQE) methods were extracted. They were combined with novel sharpness and luminosity measures based on the continuous wavelet transform (CWT) and the hue saturation value (HSV) color model, respectively. A subset of non-redundant features was selected using the fast correlation-based filter (FCBF) method. Subsequently, a multilayer perceptron (MLP) neural network was used to obtain the quality of images from the selected features. Classification results achieved 91.46% accuracy, 92.04% sensitivity, and 87.92% specificity. Results suggest that the proposed RIQA method could be applied in a more general computer-aided diagnosis system aimed at detecting a variety of retinal pathologies such as DR and age-related macular degeneration.

An Approach for Automatic Detection and Grading of Macular Edema

Prolonged Diabetes causes massive destruction to the retina, known as Diabetic Retinopathy (DR) leading to blindness. The blindness due to DR may consequence from several factors such as Blood vessel (BV) leakage, new BV formation on retina. The effects become more threatening when abnormalities involves the macular region. Here automatic analysis of fundus images becomes important. This system checks for any abnormality and help ophthalmologists in decision making and to analyze more number of cases. The main objective of this chapter is to explore image processing tools for automatic detection and grading macular edema in fundus images.

Fast detection of the main anatomical structures in digital retinal images based on intra- and inter-structure relational knowledge

BACKGROUND AND OBJECTIVE

The anatomical structure detection in retinal images is an open problem. However, most of the works in the related literature are oriented to the detection of each structure individually or assume the previous detection of a structure which is used as a reference. The objective of this paper is to obtain simultaneous detection of the main retinal structures (optic disc, macula, network of vessels and vascular bundle) in a fast and robust way.

METHODS

We propose a new methodology oriented to accomplish the mentioned objective. It consists of two stages. In an initial stage, a set of operators is applied to the retinal image. Each operator uses intra-structure relational knowledge in order to produce a set of candidate blobs that belongs to the desired structure. In a second stage, a set of tuples is created, each of which contains a different combination of the candidate blobs. Next, filtering operators, using inter-structure relational knowledge, are used in order to find the winner tuple. A method using template matching and mathematical morphology is implemented following the proposed methodology.

RESULTS

A success is achieved if the distance between the automatically detected blob center and the actual structure center is less than or equal to one optic disc radius. The success rates obtained in the different public databases analyzed were: MESSIDOR (99.33%, 98.58%, 97.92%), DIARETDB1 (96.63%, 100%, 97.75%), DRIONS (100%, n/a, 100%) and ONHSD (100%, 98.85%, 97.70%) for optic disc (OD), macula (M) and vascular bundle (VB), respectively. Finally, the overall success rate obtained in this study for each structure was: 99.26% (OD), 98.69% (M) and 98.95% (VB). The average time of processing per image was 4.16 ± 0.72 s.

CONCLUSIONS

The main advantage of the use of inter-structure relational knowledge was the reduction of the number of false positives in the detection process. The implemented method is able to simultaneously detect four structures. It is fast, robust and its detection results are competitive in relation to other methods of the recent literature.

Localization of optic disc and fovea in retinal images using intensity based line scanning analysis

Accurate detection of diabetic retinopathy (DR) mainly depends on identification of retinal landmarks such as optic disc and fovea. Present methods suffer from challenges like less accuracy and high computational complexity. To address this issue, this paper presents a novel approach for fast and accurate localization of optic disc (OD) and fovea using one-dimensional scanned intensity profile analysis. The proposed method utilizes both time and frequency domain information effectively for localization of OD. The final OD center is located using signal peak-valley detection in time domain and discontinuity detection in frequency domain analysis. However, with the help of detected OD location, the fovea center is located using signal valley analysis. Experiments were conducted on MESSIDOR dataset, where OD was successfully located in 1197 out of 1200 images (99.75%) and fovea in 1196 out of 1200 images (99.66%) with an average computation time of 0.52s. The large scale evaluation has been carried out extensively on nine publicly available databases. The proposed method is highly efficient in terms of quickly and accurately localizing OD and fovea structure together compared with the other state-of-the-art methods.

A review on automatic analysis techniques for color fundus photographs

In this paper, we give a review on automatic image processing tools to recognize diseases causing specific distortions in the human retina. After a brief summary of the biology of the retina, we give an overview of the types of lesions that may appear as biomarkers of both eye and non-eye diseases. We present several state-of-the-art procedures to extract the anatomic components and lesions in color fundus photographs and decision support methods to help clinical diagnosis. We list publicly available databases and appropriate measurement techniques to compare quantitatively the performance of these approaches. Furthermore, we discuss on how the performance of image processing-based systems can be improved by fusing the output of individual detector algorithms. Retinal image analysis using mobile phones is also addressed as an expected future trend in this field.

Optic Disc Localization Using Directional Models

Reliable localization of the optic disc (OD) is important for retinal image analysis and ophthalmic pathology screening. This paper presents a novel method to automatically localize ODs in retinal fundus images based on directional models. According to the characteristics of retina vessel networks, such as their origin at the OD and parabolic shape of the main vessels, a global directional model, named the relaxed biparabola directional model, is first built. In this model, the main vessels are modeled by using two parabolas with a shared vertex and different parameters. Then, a local directional model, named the disc directional model, is built to characterize the local vessel convergence in the OD as well as the shape and the brightness of the OD. Finally, the global and the local directional models are integrated to form a hybrid directional model, which can exploit the advantages of the global and local models for highly accurate OD localization. The proposed method is evaluated on nine publicly available databases, and achieves an accuracy of 100% for each database, which demonstrates the effectiveness of the proposed OD localization method.

Segmentation and classification of bright lesions to diagnose diabetic retinopathy in retinal images

In view of predicting bright lesions such as hard exudates, cotton wool spots, and drusen in retinal images, three different segmentation techniques have been proposed and their effectiveness is compared with existing segmentation techniques. The benchmark images with annotations present in the structured analysis of the retina (STARE) database is considered for testing the proposed techniques. The proposed segmentation techniques such as region growing (RG), region growing with background correction (RGWBC), and adaptive region growing with background correction (ARGWBC) have been used, and the effectiveness of the algorithms is compared with existing fuzzy-based techniques. Images of eight categories of various annotations and 10 images in each category have been used to test the consistency of the proposed algorithms. Among the proposed techniques, ARGWBC has been identified to be the best method for segmenting the bright lesions based on its sensitivity, specificity, and accuracy. Fifteen different features are extracted from retinal images for the purpose of identification and classification of bright lesions. Feedforward backpropagation neural network (FFBPNN) and pattern recognition neural network (PRNN) are used for the classification of normal/abnormal images. Probabilistic neural network (PNN), radial basis exact fit (RBE), radial basis fewer neurons (RB), and FFBPNN are used for further bright lesion classification and achieve 100% accuracy.

Detection of retinal capillary nonperfusion in fundus fluorescein angiogram of diabetic retinopathy

INTRODUCTION

Retinal capillary nonperfusion (CNP) is one of the retinal vascular diseases in diabetic retinopathy (DR) patients. As there is no comprehensive detection technique to recognize CNP areas, we proposed a different method for computing detection of ischemic retina, non-perfused (NP) regions, in fundus fluorescein angiogram (FFA) images.

METHODS

Whilst major vessels appear as ridges, non-perfused areas are usually observed as ponds that are surrounded by healthy capillaries in FFA images. A new technique using homomorphic filtering to correct light illumination and detect the ponds surrounded in healthy capillaries on FFA images was designed and applied on DR fundus images. These images were acquired from the diabetic patients who had referred to the Nikookari hospital and were diagnosed for diabetic retinopathy during one year. Our strategy was screening the whole image with a fixed window size, which is small enough to enclose areas with identified topographic characteristics. To discard false nominees, we also performed a thresholding operation on the screen and marked images. To validate its performance we applied our detection algorithm on 41 FFA diabetic retinopathy fundus images in which the CNP areas were manually delineated by three clinical experts.

RESULTS

Lesions were found as smooth regions with very high uniformity, low entropy, and small intensity variations in FFA images. The results of automated detection method were compared with manually marked CNP areas so achieved sensitivity of 81%, specificity of 78%, and accuracy of 91%.The result was present as a Receiver operating character (ROC) curve, which has an area under the curve (AUC) of 0.796 with 95% confidence intervals.

CONCLUSION

This technique introduced a new automated detection algorithm to recognize non-perfusion lesions on FFA. This has potential to assist detecting and managing of ischemic retina and may be incorporated into automated grading diabetic retinopathy structures.

Application of different imaging modalities for diagnosis of Diabetic Macular Edema: A review

Diabetic Macular Edema (DME) is caused by accumulation of extracellular fluid from hyperpermeable capillaries within the macula. DME is one of the leading causes of blindness among Diabetes Mellitus (DM) patients. Early detection followed by laser photocoagulation can save the visual loss. This review discusses various imaging modalities viz. biomicroscopy, Fluorescein Angiography (FA), Optical Coherence Tomography (OCT) and colour fundus photographs used for diagnosis of DME. Various automated DME grading systems using retinal fundus images, associated retinal image processing techniques for fovea, exudate detection and segmentation are presented. We have also compared various imaging modalities and automated screening methods used for DME grading. The reviewed literature indicates that FA and OCT identify DME related changes accurately. FA is an invasive method, which uses fluorescein dye, and OCT is an expensive imaging method compared to fundus photographs. Moreover, using fundus images DME can be identified and automated. DME grading algorithms can be implemented for telescreening. Hence, fundus imaging based DME grading is more suitable and affordable method compared to biomicroscopy, FA, and OCT modalities.

Analysis of Fundus Fluorescein Angiogram Based on the Hessian Matrix of Directional Curvelet Sub-bands and Distance Regularized Level Set Evolution

This paper presents a new procedure for automatic extraction of the blood vessels and optic disk (OD) in fundus fluorescein angiogram (FFA). In order to extract blood vessel centerlines, the algorithm of vessel extraction starts with the analysis of directional images resulting from sub-bands of fast discrete curvelet transform (FDCT) in the similar directions and different scales. For this purpose, each directional image is processed by using information of the first order derivative and eigenvalues obtained from the Hessian matrix. The final vessel segmentation is obtained using a simple region growing algorithm iteratively, which merges centerline images with the contents of images resulting from modified top-hat transform followed by bit plane slicing. After extracting blood vessels from FFA image, candidates regions for OD are enhanced by removing blood vessels from the FFA image, using multi-structure elements morphology, and modification of FDCT coefficients. Then, canny edge detector and Hough transform are applied to the reconstructed image to extract the boundary of candidate regions. At the next step, the information of the main arc of the retinal vessels surrounding the OD region is used to extract the actual location of the OD. Finally, the OD boundary is detected by applying distance regularized level set evolution. The proposed method was tested on the FFA images from angiography unit of Isfahan Feiz Hospital, containing 70 FFA images from different diabetic retinopathy stages. The experimental results show the accuracy more than 93% for vessel segmentation and more than 87% for OD boundary extraction.

Optic disc detection and boundary extraction in retinal images

With the development of digital image processing, analysis and modeling techniques, automatic retinal image analysis is emerging as an important screening tool for early detection of ophthalmologic disorders such as diabetic retinopathy and glaucoma. In this paper, a robust method for optic disc detection and extraction of the optic disc boundary is proposed to help in the development of computer-assisted diagnosis and treatment of such ophthalmic disease. The proposed method is based on morphological operations, smoothing filters, and the marker controlled watershed transform. Internal and external markers are used to first modify the gradient magnitude image and then the watershed transformation is applied on this modified gradient magnitude image for boundary extraction. This method has shown significant improvement over existing methods in terms of detection and boundary extraction of the optic disc. The proposed method has optic disc detection success rate of 100%, 100%, 100% and 98.9% for the DRIVE, Shifa, CHASE_DB1, and DIARETDB1 databases, respectively. The optic disc boundary detection achieved an average spatial overlap of 61.88%, 70.96%, 45.61%, and 54.69% for these databases, respectively, which are higher than currents methods.

Obtaining optic disc center and pixel region by automatic thresholding methods on morphologically processed fundus images

Development of automatic retinal disease diagnosis systems based on retinal image computer analysis can provide remarkably quicker screening programs for early detection. Such systems are mainly focused on the detection of the earliest ophthalmic signs of illness and require previous identification of fundal landmark features such as optic disc (OD), fovea or blood vessels. A methodology for accurate center-position location and OD retinal region segmentation on digital fundus images is presented in this paper. The methodology performs a set of iterative opening-closing morphological operations on the original retinography intensity channel to produce a bright region-enhanced image. Taking blood vessel confluence at the OD into account, a 2-step automatic thresholding procedure is then applied to obtain a reduced region of interest, where the center and the OD pixel region are finally obtained by performing the circular Hough transform on a set of OD boundary candidates generated through the application of the Prewitt edge detector. The methodology was evaluated on 1200 and 1748 fundus images from the publicly available MESSIDOR and MESSIDOR-2 databases, acquired from diabetic patients and thus being clinical cases of interest within the framework of automated diagnosis of retinal diseases associated to diabetes mellitus. This methodology proved highly accurate in OD-center location: average Euclidean distance between the methodology-provided and actual OD-center position was 6.08, 9.22 and 9.72 pixels for retinas of 910, 1380 and 1455 pixels in size, respectively. On the other hand, OD segmentation evaluation was performed in terms of Jaccard and Dice coefficients, as well as the mean average distance between estimated and actual OD boundaries. Comparison with the results reported by other reviewed OD segmentation methodologies shows our proposal renders better overall performance. Its effectiveness and robustness make this proposed automated OD location and segmentation method a suitable tool to be integrated into a complete prescreening system for early diagnosis of retinal diseases.

Automated detection of fovea in fundus images based on vessel-free zone and adaptive Gaussian template

This study developed a computerised method for fovea centre detection in fundus images. In the method, the centre of the optic disc was localised first by the template matching method, the disc-fovea axis (a line connecting the optic disc centre and the fovea) was then determined by searching the vessel-free region, and finally the fovea centre was detected by matching the fovea template around the centre of the axis. Adaptive Gaussian templates were used to localise the centres of the optic disc and fovea for the images with different resolutions. The proposed method was evaluated using three publicly available databases (DIARETDB0, DIARETDB1 and MESSIDOR), which consisted of a total of 1419 fundus images with different resolutions. The proposed method obtained the fovea detection accuracies of 93.1%, 92.1% and 97.8% for the DIARETDB0, DIARETDB1 and MESSIDOR databases, respectively. The overall accuracy of the proposed method was 97.0% in this study.

Computer aided quantification for retinal lesions in patients with moderate and severe non-proliferative diabetic retinopathy: a retrospective cohort study

BACKGROUND

Detection of retinal lesions like micro-aneurysms and exudates are important for the clinical diagnosis of diabetes retinopathy. The traditional subjective judgments by clinicians are dependent on their experience and can be subject to lack of consistency and therefore a quantification method is worthwhile.

METHODS

In this study, 10 moderate non-proliferative diabetes retinopathy (NPDR) patients and 10 severe NPDR ones were retrospectively selected as a cohort. Mathematical morphological methods were used for automatic segmentation of lesions. For exudates detection, images were pre-processed with adaptive histogram equalization to enhance contrast, then binary images for area calculation were obtained by threshold classification. For micro-aneurysms detection, the images were pre-processed by top-hat and bottom-hat transformation, then Otsu method and Hough transform were used to classify micro-aneurysms. Post-processing morphological methods were used to preclude the false positive noise.

RESULTS

After segmentation, the area of exuduates divided by optic disk area (exudates/disk ratio) and counts of microaneurysms were quantified and compared between the moderate and severe non-proliferative diabetic retinopathy groups, which had significant difference(P < 0.05).

CONCLUSIONS

In conclusion, morphological features of lesion might be an image marker for NPDR grading and computer aided quantification of retinal lesion could be a practical way for clinicians to better investigates diabetic retinopathy.

Identification and localization of fovea on colour fundus images using blur scales

Identification of retinal landmarks is an important step in the extraction of anomalies in retinal fundus images. In the current study, we propose a technique to identify and localize the position of macula and hence the fovea avascular zone, in colour fundus images. The proposed method, based on varying blur scales in images, is independent of the location of other anatomical landmarks present in the fundus images. Experimental results have been provided using the open database MESSIDOR by validating our segmented regions using the dice coefficient, with ground truth segmentation provided by a human expert. Apart from testing the images on the entire MESSIDOR database, the proposed technique was also validated using 50 normal and 50 diabetic retinopathy chosen digital fundus images from the same database. A maximum overlap accuracy of 89.6%−93.8% and locational accuracy of 94.7%−98.9% was obtained for identification and localization of the fovea.

Computer-aided diagnosis of proliferative diabetic retinopathy via modeling of the major temporal arcade in retinal fundus images

Monitoring the openness of the major temporal arcade (MTA) and how it changes over time could facilitate diagnosis and treatment of proliferative diabetic retinopathy (PDR). We present methods for user-guided semiautomated modeling and measurement of the openness of the MTA based on Gabor filters for the detection of retinal vessels, morphological image processing, and a form of the generalized Hough transform for the detection of parabolas. The methods, implemented via a graphical user interface, were tested with retinal fundus images of 11 normal individuals and 11 patients with PDR in the present pilot study on potential clinical application. A method of arcade angle measurement was used for comparative analysis. The results using the openness parameters of single- and dual-parabolic models as well as the arcade angle measurements indicate areas under the receiver operating characteristics of A z = 0.87, 0.82, and 0.80, respectively. The proposed methods are expected to facilitate quantitative analysis of the architecture of the MTA, as well as assist in detection and diagnosis of PDR.

Computer-aided diagnosis of diabetic retinopathy: a review

Diabetes mellitus may cause alterations in the retinal microvasculature leading to diabetic retinopathy. Unchecked, advanced diabetic retinopathy may lead to blindness. It can be tedious and time consuming to decipher subtle morphological changes in optic disk, microaneurysms, hemorrhage, blood vessels, macula, and exudates through manual inspection of fundus images. A computer aided diagnosis system can significantly reduce the burden on the ophthalmologists and may alleviate the inter and intra observer variability. This review discusses the available methods of various retinal feature extractions and automated analysis.

Computer-aided diagnosis of proliferative diabetic retinopathy

Monitoring the openness of the major temporal arcade (MTA) and how it changes over time could facilitate improved diagnosis and timely treatment of proliferative diabetic retinopathy (PDR). We present methods for user-guided modeling and measurement of the openness of the MTA based on a form of the generalized Hough transform for the detection of parabolas, and to compare it with a method of arcade angle measurement. The methods, implemented via a graphical user interface, were tested with retinal fundus images of 10 normal individuals and 15 patients with PDR. The results using the openness parameters of single- and dual-parabolic models as well as the arcade angle measurements indicate areas under the receiver operating characteristics of A(z)= 0.94, 0.87, and 0.84, respectively. The proposed methods should facilitate improved quantitative analysis of the architecture of the MTA, as well as assist in detection, diagnosis, and improved treatment of PDR.

Automatic localization of retinal landmarks

Retinal landmark detection is a key step in retinal screening and computer-aided diagnosis for different types of eye diseases, such as glaucomma, age-related macular degeneration(AMD) and diabetic retinopathy. In this paper, we propose a semantic image transformation(SIT) approach for retinal representation and automatic landmark detection. The proposed SIT characterizes the local statistics of a fundus image and boosts the intrinsic retinal structures, such as optic disc(OD), macula. We propose our salient OD and macular models based on SIT for retinal landmark detection. Experiments on 5928 images show that our method achieves an accuracy of 99.44% in the detection of OD and an accuracy of 93.49% in the detection of macula, while having an accuracy of 97.33% for left and right eye classification. The proposed SIT can automatically detect the retinal landmarks and be useful for further eye-disease screening and diagnosis.

Automatic detection of the macula in retinal fundus images using seeded mode tracking approach

The macula is the part of the eye responsible for central high acuity vision. Detection of the macula is an important task in retinal image processing as a landmark for subsequent disease assessment, such as for age-related macula degeneration. In this paper, we have presented an approach to automatically determine the macula centre in retinal fundus images. First contextual information on the image is combined with a statistical model to obtain an approximate macula region of interest localization. Subsequently, we propose the use of a seeded mode tracking technique to locate the macula centre. The proposed approach is tested on a large dataset composed of 482 normal images and 162 glaucoma images from the ORIGA database and an additional 96 AMD images. The results show a ROI detection of 97.5%, and 90.5% correct detection of the macula within 1/3DD from a manual reference, which outperforms other current methods. The results are promising for the use of the proposed approach to locate the macula for the detection of macula diseases from retinal images.

Diabetic retinopathy grading by digital curvelet transform

One of the major complications of diabetes is diabetic retinopathy. As manual analysis and diagnosis of large amount of images are time consuming, automatic detection and grading of diabetic retinopathy are desired. In this paper, we use fundus fluorescein angiography and color fundus images simultaneously, extract 6 features employing curvelet transform, and feed them to support vector machine in order to determine diabetic retinopathy severity stages. These features are area of blood vessels, area, regularity of foveal avascular zone, and the number of micro-aneurisms therein, total number of micro-aneurisms, and area of exudates. In order to extract exudates and vessels, we respectively modify curvelet coefficients of color fundus images and angiograms. The end points of extracted vessels in predefined region of interest based on optic disk are connected together to segment foveal avascular zone region. To extract micro-aneurisms from angiogram, first extracted vessels are subtracted from original image, and after removing detected background by morphological operators and enhancing bright small pixels, micro-aneurisms are detected. 70 patients were involved in this study to classify diabetic retinopathy into 3 groups, that is, (1) no diabetic retinopathy, (2) mild/moderate nonproliferative diabetic retinopathy, (3) severe nonproliferative/proliferative diabetic retinopathy, and our simulations show that the proposed system has sensitivity and specificity of 100% for grading.

Detecting optic disc on asians by multiscale gaussian filtering

The optic disc (OD) is an important anatomical feature in retinal images, and its detection is vital for developing automated screening programs. Currently, there is no algorithm designed to automatically detect the OD in fundus images captured from Asians which are larger and have thicker vessels compared to Caucasians. In this paper, we propose such a method to complement current algorithms using two steps: OD vessel candidate detection and OD vessel candidate matching. The first step is achieved with multiscale Gaussian filtering, scale production, and double thresholding to initially extract the vessels' directional map of various thicknesses. The map is then thinned before another threshold is applied to remove pixels with low intensities. This result forms the OD vessel candidates. In the second step, a Vessels' Directional Matched Filter (VDMF) of various dimensions is applied to the candidates to be matched, and the pixel with the smallest difference designated the OD center. We tested the proposed method on a new database consisting of 402 images from a diabetic retinopathy (DR) screening programme consisting of Asians. The OD center was successfully detected with an accuracy of 99.25% (399/402).

Automatic glaucoma diagnosis from fundus image

Glaucoma is currently diagnosed by glaucoma specialists using specialized imaging devices like HRT and OCT. Fundus imaging is a modality widely used in primary healthcare. An automatic glaucoma diagnosis system based on fundus image can be deployed to primary healthcare clinics and has potential for early disease diagnosis. A mass glaucoma screening program can also be facilitated using such a system. We present an automatic fundus image based cup-to-disc ratio measurement system; and demonstrate its potential for automatic objective glaucoma diagnosis and screening. It provides strong support to use fundus image as the modality for automatic glaucoma diagnosis.

Fast localization and segmentation of optic disk in retinal images using directional matched filtering and level sets

The optic disk (OD) center and margin are typically requisite landmarks in establishing a frame of reference for classifying retinal and optic nerve pathology. Reliable and efficient OD localization and segmentation are important tasks in automatic eye disease screening. This paper presents a new, fast, and fully automatic OD localization and segmentation algorithm developed for retinal disease screening. First, OD location candidates are identified using template matching. The template is designed to adapt to different image resolutions. Then, vessel characteristics (patterns) on the OD are used to determine OD location. Initialized by the detected OD center and estimated OD radius, a fast, hybrid level-set model, which combines region and local gradient information, is applied to the segmentation of the disk boundary. Morphological filtering is used to remove blood vessels and bright regions other than the OD that affect segmentation in the peripapillary region. Optimization of the model parameters and their effect on the model performance are considered. Evaluation was based on 1200 images from the publicly available MESSIDOR database. The OD location methodology succeeded in 1189 out of 1200 images (99% success). The average mean absolute distance between the segmented boundary and the reference standard is 10% of the estimated OD radius for all image sizes. Its efficiency, robustness, and accuracy make the OD localization and segmentation scheme described herein suitable for automatic retinal disease screening in a variety of clinical settings.

Fovea center detection based on the retina anatomy and mathematical morphology

In this work, we present a new fovea center detection method for color eye fundus images. This method is based on known anatomical constraints on the relative locations of retina structures, and mathematical morphology. The detection of this anatomical feature is a prerequisite for the computer aided diagnosis of several retinal diseases, such as Diabetic Macular Edema. The proposed method is adaptive to local illumination changes, and it is robust to local disturbances introduced by pathologies in digital color eye fundus images (e.g. exudates). Our experimental results using the DRIVE image database indicate that our method is able to detect the fovea center in 37 out of 37 images (i.e. with a success rate of 100%). Using the DIARETDB1 database, our method was able to detect the fovea center in 92.13% of all tested cases (i.e. in 82 out of 89 images). These results indicate that our approach potentially can achieve a better performance than comparable methods proposed in the literature.

Accurate and efficient optic disc detection and segmentation by a circular transformation

Under the framework of computer-aided diagnosis, this paper presents an accurate and efficient optic disc (OD) detection and segmentation technique. A circular transformation is designed to capture both the circular shape of the OD and the image variation across the OD boundary simultaneously. For each retinal image pixel, it evaluates the image variation along multiple evenly-oriented radial line segments of specific length. The pixels with the maximum variation along all radial line segments are determined, which can be further exploited to locate both the OD center and the OD boundary accurately. Experiments show that OD detection accuracies of 99.75%, 97.5%, and 98.77% are obtained for the STARE dataset, the ARIA dataset, and the MESSIDOR dataset, respectively, and the OD center error lies around six pixels for the STARE dataset and the ARIA dataset which is much smaller than that of state-of-the-art methods ranging 14-29 pixels. In addition, the OD segmentation accuracies of 93.4% and 91.7% are obtained for STARE dataset and ARIA dataset, respectively, that consists of many severely degraded images of pathological retinas that state-of-the-art methods cannot segment properly. Furthermore, the algorithm runs in 5 s, which is substantially faster than many of the state-of-the-art methods.