Hybrid Nature Inspired SMO-GBM Classifier for Exudate Classification on Fundus Retinal Images

Background: The diabetic retinopathy can result in loss of vision if not detected in the earlier stages. Exudates are the lesions which play a crucial role in early diagnosis of diabetic retinopathy. The localization of exudates lesions with high values of performance metrics is complicated due to presence of blood vessels and other noisy artifacts. Method: We present computer aided system for classification of retinal fundus images using a novel nature inspired spider monkey optimization for parameter tuning of gradient boosting machines classifier. The image enhancement has been performed with histogram equalization and contourlet transform. The pixels belonging to optic disc region are detected and eliminated using circular Hough transform and Otsu's segmentation method. We have employed Kirsch's matrices for blood vessel detection. The GLCM based feature vector extraction has been employed for textural features. The classification has been performed with hybrid SMO-GBM classifier. Result: We have utilized the STARE database for validation of proposed technique. The proposed system can effectively classify entire image set from test data. The SMO-GBM classifier can further sub-segregate into sub classes with an average accuracy of 97.5%. Conclusion: The proposed approach provides detection and grading of diabetic retinopathy. The abnormality is further categories as soft, moderate and severe. The hybrid SMO-GBM classifier yields a better statistical metrics than the existing exudates classification approaches.     

A Comprehensive Texture Segmentation Framework for Segmentation of Capillary Non-Perfusion Regions in Fundus Fluorescein Angiograms

Capillary non-perfusion (CNP) in the retina is a characteristic feature used in the management of a wide range of retinal diseases. There is no well-established computation tool for assessing the extent of CNP. We propose a novel texture segmentation framework to address this problem. This framework comprises three major steps: pre-processing, unsupervised total variation texture segmentation, and supervised segmentation. It employs a state-of-the-art multiphase total variation texture segmentation model which is enhanced by new kernel based region terms. The model can be applied to texture and intensity-based multiphase problems. A supervised segmentation step allows the framework to take expert knowledge into account, an AdaBoost classifier with weighted cost coefficient is chosen to tackle imbalanced data classification problems. To demonstrate its effectiveness, we applied this framework to 48 images from malarial retinopathy and 10 images from ischemic diabetic maculopathy. The performance of segmentation is satisfactory when compared to a reference standard of manual delineations: accuracy, sensitivity and specificity are 89.0%, 73.0%, and 90.8% respectively for the malarial retinopathy dataset and 80.8%, 70.6%, and 82.1% respectively for the diabetic maculopathy dataset. In terms of region-wise analysis, this method achieved an accuracy of 76.3% (45 out of 59 regions) for the malarial retinopathy dataset and 73.9% (17 out of 26 regions) for the diabetic maculopathy dataset. This comprehensive segmentation framework can quantify capillary non-perfusion in retinopathy from two distinct etiologies, and has the potential to be adopted for wider applications.     

Accurate Image Analysis of the Retina Using Hessian Matrix and Binarisation of Thresholded Entropy with Application of Texture Mapping

In this paper, we demonstrate a comprehensive method for segmenting the retinal vasculature in camera images of the fundus. This is of interest in the area of diagnostics for eye diseases that affect the blood vessels in the eye. In a departure from other state-of-the-art methods, vessels are first pre-grouped together with graph partitioning, using a spectral clustering technique based on morphological features. Local curvature is estimated over the whole image using eigenvalues of Hessian matrix in order to enhance the vessels, which appear as ridges in images of the retina. The result is combined with a binarized image, obtained using a threshold that maximizes entropy, to extract the retinal vessels from the background. Speckle type noise is reduced by applying a connectivity constraint on the extracted curvature based enhanced image. This constraint is varied over the image according to each region''s predominant blood vessel size. The resultant image exhibits the central light reflex of retinal arteries and veins, which prevents the segmentation of whole vessels. To address this, the earlier entropy-based binarization technique is repeated on the original image, but crucially, with a different threshold to incorporate the central reflex vessels. The final segmentation is achieved by combining the segmented vessels with and without central light reflex. We carry out our approach on DRIVE and REVIEW, two publicly available collections of retinal images for research purposes. The obtained results are compared with state-of-the-art methods in the literature using metrics such as sensitivity (true positive rate), selectivity (false positive rate) and accuracy rates for the DRIVE images and measured vessel widths for the REVIEW images. Our approach out-performs the methods in the literature.     

Hyperspectral computed tomographic imaging spectroscopy of vascular oxygen gradients in the rabbit retina in vivo

Diagnosis of retinal vascular diseases depends on ophthalmoscopic findings that most often occur after severe visual loss (as in vein occlusions) or chronic changes that are irreversible (as in diabetic retinopathy). Despite recent advances, diagnostic imaging currently reveals very little about the vascular function and local oxygen delivery. One potentially useful measure of vascular function is measurement of hemoglobin oxygen content. In this paper, we demonstrate a novel method of accurately, rapidly and easily measuring oxygen saturation within retinal vessels using in vivo imaging spectroscopy. This method uses a commercially available fundus camera coupled to two-dimensional diffracting optics that scatter the incident light onto a focal plane array in a calibrated pattern. Computed tomographic algorithms are used to reconstruct the diffracted spectral patterns into wavelength components of the original image. In this paper the spectral components of oxy- and deoxyhemoglobin are analyzed from the vessels within the image. Up to 76 spectral measurements can be made in only a few milliseconds and used to quantify the oxygen saturation within the retinal vessels over a 10-15 degree field. The method described here can acquire 10-fold more spectral data in much less time than conventional oximetry systems (while utilizing the commonly accepted fundus camera platform). Application of this method to animal models of retinal vascular disease and clinical subjects will provide useful and novel information about retinal vascular disease and physiology.     

Automatic non-proliferative diabetic retinopathy screening system based on color fundus image

Background

Non-proliferative diabetic retinopathy is the early stage of diabetic retinopathy. Automatic detection of non-proliferative diabetic retinopathy is significant for clinical diagnosis, early screening and course progression of patients.

Methods

This paper introduces the design and implementation of an automatic system for screening non-proliferative diabetic retinopathy based on color fundus images. Firstly, the fundus structures, including blood vessels, optic disc and macula, are extracted and located, respectively. In particular, a new optic disc localization method using parabolic fitting is proposed based on the physiological structure characteristics of optic disc and blood vessels. Then, early lesions, such as microaneurysms, hemorrhages and hard exudates, are detected based on their respective characteristics. An equivalent optical model simulating human eyes is designed based on the anatomical structure of retina. Main structures and early lesions are reconstructed in the 3D space for better visualization. Finally, the severity of each image is evaluated based on the international criteria of diabetic retinopathy.

Results

The system has been tested on public databases and images from hospitals. Experimental results demonstrate that the proposed system achieves high accuracy for main structures and early lesions detection. The results of severity classification for non-proliferative diabetic retinopathy are also accurate and suitable.

Conclusions

Our system can assist ophthalmologists for clinical diagnosis, automatic screening and course progression of patients.

     

SDOCT imaging to identify macular pathology in patients diagnosed with diabetic maculopathy by a digital photographic retinal screening programme

Introduction

Diabetic macular edema (DME) is an important cause of vision loss. England has a national systematic photographic retinal screening programme to identify patients with diabetic eye disease. Grading retinal photographs according to this national protocol identifies surrogate markers for DME. We audited a care pathway using a spectral-domain optical coherence tomography (SDOCT) clinic to identify macular pathology in this subset of patients.

Methods

A prospective audit was performed of patients referred from screening with mild to moderate non-proliferative diabetic retinopathy (R1) and surrogate markers for diabetic macular edema (M1) attending an SDOCT clinic. The SDOCT images were graded by an ophthalmologist as SDOCT positive, borderline or negative. SDOCT positive patients were referred to the medical retina clinic. SDOCT negative and borderline patients were further reviewed in the SDOCT clinic in 6 months.

Results

From a registered screening population of 17 551 patients with diabetes mellitus, 311 patients met the inclusion criteria between (March 2008 and September 2009). We analyzed images from 311 patients’ SDOCT clinic episodes. There were 131 SDOCT negative and 12 borderline patients booked for revisit in the OCT clinic. Twenty-four were referred back to photographic screening for a variety of reasons. A total of 144 were referred to ophthalmology with OCT evidence of definite macular pathology requiring review by an ophthalmologist.

Discussion

This analysis shows that patients with diabetes, mild to moderate non-proliferative diabetic retinopathy (R1) and evidence of diabetic maculopathy on non-stereoscopic retinal photographs (M1) have a 42.1% chance of having no macular edema on SDOCT imaging as defined by standard OCT definitions of DME when graded by a retinal specialist. SDOCT imaging is a useful adjunct to colour fundus photography in screening for referable diabetic maculopathy in our screening population.     

Apoptotic factors (Bcl-2 and Bax) and diabetic retinopathy in type 2 diabetes

The expression of apoptotic factors Bcl-2 and Bax were studied in the conjunctiva of diabetic patients with and without retinopathy. All patients underwent a complete ophthalmic examination including ocular fundus and retinal fluorescein angiography. The indirect immunoperoxidase method was performed on 15 normal conjunctiva taken during cataract surgery (group 1), on 40 eyes of 40 patients with type 2 diabetes without diabetic retinopathy (group 2) and 13 eyes of 13 patients with diabetic retinopathy (group 3). In normal human conjunctiva, Bax showed positive expression in epithelial, vascular and stromal cells whereas Bcl-2 staining was negative. In the conjunctiva of diabetic patients without diabetic retinopathy, Bax was widely, and strongly, expressed in epithelial cells, vascular endothelial cells, fibroblasts and infiltrating cells such as macrophages. For patients with diabetic retinopathy, Bax was consistently strong to very strong. Bcl-2 protein expression became weak to negative for diabetic patients both with and without diabetic retinopathy. Immunoreactivity was not correlated between Bcl-2 and Bax in the conjunctiva of diabetic patients. Bax was always localized in tissues characterized by a high rate of apoptosis, whereas, Bcl-2 was absent. Our results suggest that diabetic human conjunctiva, with its inflammatory phenomena, is considered as a privileged target for programmed cell death.     

高压氧在治疗糖尿病视网膜病变中的应用进展

糖尿病视网膜病变是糖尿病最常见、最主要的微血管并发症之一,其高发病率、高致盲率特征严重威胁着人类的健康和生存质量。长期的高糖状态导致视网膜缺血缺氧是糖尿病视网膜病变的主要发病原因。控制高血糖和改善组织缺氧无疑是防治糖尿病微血管病变的有效方法之一。而高压氧治疗是许多急慢性疾病的首选或辅助治疗方法。已有大量证据表明,高压氧治疗对视网膜静脉阻塞及黄斑囊样水肿、缺血性视神经病变、中心性浆液性脉络膜视网膜病变等眼病安全有效。本文就高压氧在糖尿病视网膜病变中的应用进行综述和讨论。     

Fundus Retinal Vessels Image Segmentation Method Based on Improved U-Net

ObjectivesAlthough the segmentation of retinal vessels in the fundus is of great significance for screening and diagnosing retinal vascular diseases, it remains difficult to detect the low contrast and the information around the lesions provided by retinal vessels in the fundus and to locate and segment micro-vessels in the fine-grained area. To overcome this problem, we propose herein an improved U-Net segmentation method NoL-UNet.Material and methodsThis work introduces NoL-UNet. First of all, the ordinary convolution block of the U-Net network is changed to random dropout convolution blocks, which can better extract the relevant features of the image and effectively alleviate the network overfitting. Next, a NoL-Block attention mechanism added to the bottom of the encoding-decoding structure expands the receptive field and enhances the correlation of pixel information without increasing the number of parameters.ResultsThe proposed method is verified by applying it to the fundus image datasets DRIVE, CHASE_DB1, and HRF. The AUC for DRIVE, CHASE_DB1 and HRF is 0.9861, 0.9891 and 0.9893, Se for DRIVE, CHASE_DB1 and HRF is 0.8489, 0.8809 and 0.8476, and the Acc for DRIVE, CHASE_DB1 and HRF is 0.9697, 0.9826 and 0.9732, respectively. The total number of parameters is 1.70M, and for DRIVE, it takes 0.050s to segment an image.ConclusionOur method is statistically significantly different from the U-Net method, and the improved method shows superior performance with better accuracy and robustness of the model, which has good practical application in auxiliary diagnosis.     

The Reading of Components of Diabetic Retinopathy: An Evolutionary Approach for Filtering Normal Digital Fundus Imaging in Screening and Population Based Studies

In any diabetic retinopathy screening program, about two-thirds of patients have no retinopathy. However, on average, it takes a human expert about one and a half times longer to decide an image is normal than to recognize an abnormal case with obvious features. In this work, we present an automated system for filtering out normal cases to facilitate a more effective use of grading time. The key aim with any such tool is to achieve high sensitivity and specificity to ensure patients'' safety and service efficiency. There are many challenges to overcome, given the variation of images and characteristics to identify. The system combines computed evidence obtained from various processing stages, including segmentation of candidate regions, classification and contextual analysis through Hidden Markov Models. Furthermore, evolutionary algorithms are employed to optimize the Hidden Markov Models, feature selection and heterogeneous ensemble classifiers. In order to evaluate its capability of identifying normal images across diverse populations, a population-oriented study was undertaken comparing the software''s output to grading by humans. In addition, population based studies collect large numbers of images on subjects expected to have no abnormality. These studies expect timely and cost-effective grading. Altogether 9954 previously unseen images taken from various populations were tested. All test images were masked so the automated system had not been exposed to them before. This system was trained using image subregions taken from about 400 sample images. Sensitivities of 92.2% and specificities of 90.4% were achieved varying between populations and population clusters. Of all images the automated system decided to be normal, 98.2% were true normal when compared to the manual grading results. These results demonstrate scalability and strong potential of such an integrated computational intelligence system as an effective tool to assist a grading service.     

A new unsupervised feature ranking method for gene expression data based on consensus affinity

Feature selection is widely established as one of the fundamental computational techniques in mining microarray data. Due to the lack of categorized information in practice, unsupervised feature selection is more practically important but correspondingly more difficult. Motivated by the cluster ensemble techniques, which combine multiple clustering solutions into a consensus solution of higher accuracy and stability, recent efforts in unsupervised feature selection proposed to use these consensus solutions as oracles. However,these methods are dependent on both the particular cluster ensemble algorithm used and the knowledge of the true cluster number. These methods will be unsuitable when the true cluster number is not available, which is common in practice. In view of the above problems, a new unsupervised feature ranking method is proposed to evaluate the importance of the features based on consensus affinity. Different from previous works, our method compares the corresponding affinity of each feature between a pair of instances based on the consensus matrix of clustering solutions. As a result, our method alleviates the need to know the true number of clusters and the dependence on particular cluster ensemble approaches as in previous works. Experiments on real gene expression data sets demonstrate significant improvement of the feature ranking results when compared to several state-of-the-art techniques.     

Adhesion molecules (ICAM-1 and VCAM-1) and diabetic retinopathy in type 2 diabetes

The expression of intercellular adhesion molecule (ICAM-1) and vascular cell adhesion molecule (VCAM-1) were studied in the conjunctiva of diabetic patients with and without retinopathy. All patients underwent a complete ophthalmic examination including ocular fundus and retinal fluorescein angiography. The indirect immunoperoxidase method was performed on 15 normal conjunctivas taken during cataract surgery (group 1), on 40 eyes of 40 patients with type 2 diabetes without diabetic retinopathy (DR) (group 2) and 13 eyes of 13 patients with DR (group 3). ICAM-1 and VCAM-1 are located in epithelial cells, vascular endothelial cells and in stromal cells. Our results show a statistically significant increase in the immunohistochemical expression of these proteins in the conjunctiva of diabetic patients with and without DR in comparison with normal conjunctiva (P = 0.001). Noteworthy, ICAM-1 and VCAM-1 are upregulated in the conjunctiva of diabetic patients with and without retinopathy, reflecting the inflammatory nature of this condition and suggesting a possible role for these mediators in the pathogenesis of diabetic microangiopathy.     

A Multi-Branch Convolutional Neural Network for Screening and Staging of Diabetic Retinopathy Based on Wide-Field Optical Coherence Tomography Angiography

BackgroundDiabetic retinopathy (DR) is one of the major causes of blindness in adults suffering from diabetes. With the development of wide-field optical coherence tomography angiography (WF-OCTA), it is to become a gold standard for diagnosing DR. The demand for automated DR diagnosis system based on OCTA images have been fostered due to large diabetic population and pervasiveness of retinopathy cases.Materials and methodsIn this study, 288 diabetic patients and 97 healthy people were imaged by the swept-source optical coherence tomography (SS-OCT) with 12 mm × 12 mm single scan centered on the fovea. A multi-branch convolutional neural network (CNN) was proposed to classify WF-OCTA images into four grades: no DR, mild non-proliferative diabetic retinopathy (NPDR), moderate to severe NPDR, and proliferative diabetic retinopathy (PDR).ResultsThe proposed model achieved a classification accuracy of 96.11%, sensitivity of 98.08% and specificity of 89.43% in detecting DR. The accuracy of the model for DR staging is 90.56%, which is higher than that of other mainstream convolution neural network models.ConclusionThis technology enables early diagnosis and objective tracking of disease progression, which may be useful for optimal treatment to reduce vision loss.     

Retinal Vessel Width Measurement at Branchings Using an Improved Electric Field Theory-Based Graph Approach

The retinal vessel width relationship at vessel branch points in fundus images is an important biomarker of retinal and systemic disease. We propose a fully automatic method to measure the vessel widths at branch points in fundus images. The method is a graph-based method, in which a graph construction method based on electric field theory is applied which specifically deals with complex branching patterns. The vessel centerline image is used as the initial segmentation of the graph. Branching points are detected on the vessel centerline image using a set of detection kernels. Crossing points are distinguished from branch points and excluded. The electric field based graph method is applied to construct the graph. This method is inspired by the non-intersecting force lines in an electric field. At last, the method is further improved to give a consistent vessel width measurement for the whole vessel tree. The algorithm was validated on 100 artery branchings and 100 vein branchings selected from 50 fundus images by comparing with vessel width measurements from two human experts.     

葛根素对糖尿病视网膜细胞凋亡作用的研究进展

糖尿病的发病率逐年上升,其并发症的严重性日趋明显,特别是糖尿病视网膜病变导致视力下降和丧失已经引起了广泛关注,所以研究糖尿病视网膜病变的发病机制及其防治是必要的。糖尿病视网膜病变是一种多种机制共同作用的复杂性疾病,而细胞凋亡在糖尿病视网膜病变的发生和发展中起着重要的作用,所以研究细胞凋亡对糖尿病视网膜病变的治疗有着重要意义。由于细胞凋亡研究的深入,人们将注意力集中于糖尿病视网膜细胞凋亡能否得到抑制和逆转的问题上。研究发现,糖尿病视网膜病变细胞凋亡可能与视网膜新生血管形成、VEGF水平增高等因素有关。当前对葛根素的研究表明,葛根素能有效抑制视网膜新生血管形成,并且对于缺血、缺氧等因素引起的损害有很强的改善作用,葛根素还可以降低糖尿病糖基化终产物水平,甚至对视网膜超微结构的损害具有一定的保护作用,所以葛根素可能是治疗糖尿病性视网膜病变的新策略。本文就近期糖尿病视网膜病变中细胞凋亡的有关研究和葛根素的抗细胞凋亡作用做一综述,提示在糖尿病视网膜病变中葛根素的不可忽视的作用。     

The retinal renin-angiotensin system: roles of angiotensin II and aldosterone

In the present review we examine the experimental and clinical evidence for the presence of a local renin-angiotensin system within the retina. Interest in a pathogenic role for the renin-angiotensin system in retinal disease originally stemmed from observations that components of the pathway were elevated in retina during the development of certain retinal pathologies. Since then, our knowledge about the contribution of the RAS to retinal disease has greatly expanded. We discuss the known functions of the renin-angiotensin system in retinopathy of prematurity and diabetic retinopathy. This includes the promotion of retinal neovascularization, inflammation, oxidative stress and neuronal and glial dysfunction. The contribution of specific components of the renin-angiotensin system is evaluated with a particular focus on angiotensin II and aldosterone and their cognate receptors. The therapeutic utility of inhibiting key components of the renin-angiotensin system is complex, but may hold promise for the prevention and improvement of vision threatening diseases.     

Unsupervised Retinal Vessel Segmentation Using Combined Filters

Image segmentation of retinal blood vessels is a process that can help to predict and diagnose cardiovascular related diseases, such as hypertension and diabetes, which are known to affect the retinal blood vessels’ appearance. This work proposes an unsupervised method for the segmentation of retinal vessels images using a combined matched filter, Frangi’s filter and Gabor Wavelet filter to enhance the images. The combination of these three filters in order to improve the segmentation is the main motivation of this work. We investigate two approaches to perform the filter combination: weighted mean and median ranking. Segmentation methods are tested after the vessel enhancement. Enhanced images with median ranking are segmented using a simple threshold criterion. Two segmentation procedures are applied when considering enhanced retinal images using the weighted mean approach. The first method is based on deformable models and the second uses fuzzy C-means for the image segmentation. The procedure is evaluated using two public image databases, Drive and Stare. The experimental results demonstrate that the proposed methods perform well for vessel segmentation in comparison with state-of-the-art methods.     

Simultaneous visible light optical coherence tomography and near infrared OCT angiography in retinal pathologies: A case study

A dual-channel optical coherence tomography system with wavelengths in the visible and near-infrared light ranges can provide both structural and functional information for retinal microvasculature simultaneously. We applied this integrated system in an ongoing clinical study of patients with various retinal pathologies. Here, we present case study results of patients with diabetic retinopathy, central retinal vein occlusion, and sickle cell retinopathy compared to a healthy subject. For the first time, this comparison validates the system’s ability to detect structural anomalies in both en face and B-scan images with simultaneous retinal optical coherence tomography angiography and measurement of sO₂ in parafoveal vessels that are around 20–30 µm in diameter. This integrated system represents a powerful instrument with potentially far-reaching clinical implications for the early detection and diagnosis of retinal vascular diseases.     

Endoplasmic reticulum stress and inflammation: mechanisms and implications in diabetic retinopathy

The endoplasmic reticulum (ER) is the primary cellular compartment where proteins are synthesized and modified before they can be transported to their destination. Dysfunction of the ER impairs protein homeostasis and leads to the accumulation of misfolded/unfolded proteins in the ER, or ER stress. While it has long been recognized that ER stress is a major cause of conformational disorders, such as Alzheimer's disease, Huntington's disease, certain types of cancer, and type 2 diabetes, recent evidence suggests that ER stress is also implicated in many chronic inflammatory diseases. These diseases include irritable bowel syndrome, atherosclerosis, diabetic complications, and many others. Diabetic retinopathy is a common microvascular complication of diabetes, characterized by chronic inflammation, progressive damage to retinal vascular and neuronal cells, vascular leakage, and abnormal blood vessel growth (neovascularization). In this review, we discuss the role and mechanisms of ER stress in retinal inflammation and vascular damage in diabetic retinopathy.     

Automated Multi-Lesion Detection for Referable Diabetic Retinopathy in Indigenous Health Care

Diabetic Retinopathy (DR) is a complication of diabetes mellitus that affects more than one-quarter of the population with diabetes, and can lead to blindness if not discovered in time. An automated screening enables the identification of patients who need further medical attention. This study aimed to classify retinal images of Aboriginal and Torres Strait Islander peoples utilizing an automated computer-based multi-lesion eye screening program for diabetic retinopathy. The multi-lesion classifier was trained on 1,014 images from the São Paulo Eye Hospital and tested on retinal images containing no DR-related lesion, single lesions, or multiple types of lesions from the Inala Aboriginal and Torres Strait Islander health care centre. The automated multi-lesion classifier has the potential to enhance the efficiency of clinical practice delivering diabetic retinopathy screening. Our program does not necessitate image samples for training from any specific ethnic group or population being assessed and is independent of image pre- or post-processing to identify retinal lesions. In this Aboriginal and Torres Strait Islander population, the program achieved 100% sensitivity and 88.9% specificity in identifying bright lesions, while detection of red lesions achieved a sensitivity of 67% and specificity of 95%. When both bright and red lesions were present, 100% sensitivity with 88.9% specificity was obtained. All results obtained with this automated screening program meet WHO standards for diabetic retinopathy screening.