Detection Of Microaneurysms Research Articles

Detection of microaneurysms and hemorrhages based on improved Hessian matrix.

Knowing the early lesion detection of fundus images is very important to prevent blindness, and accurate lesion segmentation can provide doctors with diagnostic evidence. This study proposes a method based on improved Hessian matrix eigenvalue analysis to detect microaneurysms and hemorrhages in the fundus images of diabetic patients. A two-step method including identification of lesion candidate regions and classification of candidate regions is adopted. In the first step, the method of eigenvalue analysis based on the improved hessian matrix was applied to enhance the image preprocessed. A dual-threshold method was used for segmentation. Then, blood vessels were gradually removed to obtain the lesion candidate regions. In the second step, all candidates were classified into three categories: microaneurysms, hemorrhages and the others. The proposed method has achieved a better performance compared with the existing algorithms on accuracy rates. The classification accuracy rates of microaneurysms and hemorrhages obtained by using our method were 94.4% and 94.0%, respectively, while the classification accuracy rates obtained by using Frangi's filter based on the Hessian matrix to enhance the image were 90.9% and 92.1%. This study demonstrated a methodology for enhancing images by using eigenvalue analysis based on the improved Hessian matrix and segmentation by using double thresholds. The proposed method is beneficial to improve the detection accuracy of microaneurysms and hemorrhages in fundus images.

An efficient detection of micro aneurysms from fundus images with CDLNN algorithm

An efficient detection of micro aneurysms from fundus images with CDLNN algorithm

Retinal Based Pathology Analysis Using Deep Learning Approaches

: Deep learning is an excellent approach to biomedical image segmentation tasks that help to identify and quantify patterns. Segmentation of biomedical images typically involves partitioning an image into multiple regions representing anatomical objects of interest. Biomedical image processing is a very broad field. Here, input is given as an image to extract features from an image to detect diseases. Unsupervised learning technique is used because it enables the model to uncover trends and facts that were previously undetected on its own. Human eye retina can provide valuable information about human health. The condition of the retinal vessels has been found to be a good indicator of overall health. For the prediction of retinal disease, it is classified as a retinal background, retinal vessels and optic disc. The disease can be predicted by this classification using the Singular Spectrum Analysis, Deep Neural Network, KNN Classifier and Eclipse Fitting algorithm. Singular Spectrum analysis was carried out to predict cross-section profiles for the detection of micro aneurysms. Diabetic diseases are classified under the KNN classification. Deep Neural Network algorithm is used for predicting cardiovascular disease and other disease such as glaucoma, stroke. Using the Eclipse Fitting algorithm, the optical disc and cup pixels are segmented to check whether or not a person's health is normal.

Microaneurysm detection in fundus images based on a novel end-to-end convolutional neural network

Microaneurysms are the earliest symptom of diabetic retinopathy and play an important role in the screening of diabetic retinopathy. However, because of the complex background, automatic detection microaneurysm in fundus images is a challenging task. Firstly, motivated by the characteristics of microaneurysm, a novel deep convolutional encoder-decoder network for microaneurysm detection is designed to locate the MAs by the differences between the skip connection in the network. Then, a weighted dice loss, termed the smooth dice loss, is presented to put more focus on misclassified microaneurysms. Finally, an activation function with a long tail is used to produce an accurate probability map for MA detection. Plenty of experiments, conducted on the Retinopathy Online Challenge dataset and the e-ophtha-MA dataset, demonstrate that the proposed model achieves the comparable performance to the existing state-of-the-art methods on microaneurysm detection with only one-hundredth the running time compared with its counterparts. The proposed method is simple and effective, guarantees the performance while shortening the test time. It indicates the potential application in the auxiliary diagnosis of diabetic retinopathy screening.

Retinal Microaneurysm Detection by CNN

Diabetic Retinopathy (DR) is a fast-growing retinal disease happens as a result of exponential growth in sugar level in blood which diminishes eyesight. The severity level identification of this eye disorder is performed by ophthalmologists due to scarcity of good software for finding DR. The initial stage of diabetic retinopathy is identified by the presence of microaneurysm. This paper conducts the initial phase detection of the disease by using Convolutional Neural Network (CNN). For conducting experiment DIARETDB1 dataset used. The images from the database are resized as a preprocessing step then automatic feature extraction done by the simple CNN used. By performing training, the CNN network classifies images with and without disease. The Sensitivity, Specificity and Accuracy obtained by the technique explained is 97.62%, 100% and 97.75%.

WITHDRAWN: An efficient detection of micro aneurysms from fundus images

Diabetic Retinopathy (DR) is used for elongated occurring Diabetes, and the foremost reason for visual damage is the discrepancies in the blood vessels of the retina. As the disease is asymptomatic, it is just being diagnosed through an ophthalmologist. Nevertheless, defensive and premature diagnosis is problematic owing to the absence of changes in terms of period and price. Throughout this phase, there is an injury on the retinal image such as Microaneurysms (MA), which is one of the initially noticed medical symptoms that specify the illness. Detection of MA in the early stage is one of the Challenging and an open issue in modern Literature. An Automatic System is introduced to assist the ophthalmologist for early detection. The retinal images need to pre-process prior to performing the segmentation using the clustering approach. The segmentation attained employing Enhanced Genetic K-Means (EGK-Means) Clustering algorithm from which the features are minimized through Support Vector Machines and, finally, the categorization of the retinal images is done through the usage of region descriptors employing Decision tree algorithm. The experimental results are carried out using DiaRetDB1 and Retinopathy Online Challenge (ROC) database, and it is observed that the specificity and sensitivity of the proposed methodology are higher when matched with the existing approaches.

A Novel Diabetic Retinopathy Detection Approach Based on Deep Symmetric Convolutional Neural Network

Diabetic Retinopathy (DR) may lead to blindness in diabetic patients, which is one of the most severe eye diseases. Therefore, using automatical technology to detect DR at the early phase has very vital clinical significance. In order to detect the microaneurysms (MAs) and hard exudates (HEs) of DR, a novel detection method based on deep symmetric convolutional neural network is proposed in this paper. The symmetric convolutional structure is used to improve the effectiveness of feature extraction. The proposed method also can overcome the imbalance of positive and negative samples to avoid overfitting by increasing the width and depth of the network. Furthermore, different network structures (convolution, pooling) are used to achieve different feature filtering in the stage of feature extractions. According to the experimental results, the proposed method is superior to the state-of-the-art approach on the public dataset DIARETDB1 (DB1). The detection accuracy of the objects is 92.0%, 93.2%, 93.6%, when using different filtering structures (convolution, max-pooling, ave-pooling) respectively. The detection of microaneurysms is much improved by using ave-pooling layer for feature filtering, and the max-pooling layer can improve the detection of hard exudates.

Automated Microaneurysms Detection in Retinal Images Using Radon Transform and Supervised Learning: Application to Mass Screening of Diabetic Retinopathy

Detection of red lesions in color retinal images is a critical step to prevent the development of vision loss and blindness associated with diabetic retinopathy (DR). Microaneurysms (MAs) are the most frequently observed and are usually the first lesions to appear as a consequence of DR. Therefore, their detection is necessary for mass screening of DR. However, detecting these lesions is a challenging task because of the low image contrast, and the wide variation of imaging conditions. Recently, the emergence of computer-aided diagnosis systems offers promising approaches to detect these lesions for diagnostic purposes. In this paper we focus on developing unsupervised and supervised techniques to cope intelligently with the MAs detection problem. In the first step, the retinal images are preprocessed to remove background variation in order to achieve a high level of accuracy in the detection. In the main processing step, important landmarks such as the optic nerve head and retinal vessels are detected and masked using the Radon transform (RT) and multi-overlapping windows. Finally, the MAs are detected and numbered by using a combination of RT and a supervised support vector machine classifier. The method was tested on three publicly available datasets and a local database comprising a total of 749 images. Detection performance was evaluated using sensitivity, specificity, and FROC analysis. From the image analysis viewpoint, DR was detected with a sensitivity of 100% and a specificity of 93% on average across all of these databases. Moreover, from lesion-based analysis the proposed approach detected the MAs with sensitivity of 95.7% with an average of 7 false positives per image. These results compare favourably with the best of the published results to date.

Detection of Microaneurysms Using Grey Wolf Optimization for Early Diagnosis of Diabetic Retinopathy

The diabetic retinopathy is the leading cause of blindness worldwide, so early detection of diabetic retinopathy is necessary to reduce eye-related diseases. The accurate identification of microaneurysms is crucial for the detection of diabetic retinopathy, because it appears as the first sign of the disease. In this study, a new model is proposed to detect microaneurysms from the retinal images for early diagnosis of diabetic retinopathy. At first, the fundus images are collected from e-ophtha microaneurysms and DiaretDB1 datasets. Next, image pre-processing is accomplished using image normalization, low light image enhancement, gradient weighting and shade correction. The pre-processing methods significantly brighten the contrast of the fundus images for better visual quality and extract the hidden details of the dark conditions. In addition, Hessian based filter and Otsu threshold are used to extract the foreground objects like microaneurysms from the enhanced fundus images. At last, Grey Wolf Optimization (GWO) is used to predict the correctness of segmented microaneurysms candidates. The experimental results have revealed that the proposed model enhanced the microaneurysms detection up to 0.06-0.30 f-score value compared to the other existing models local convergence index features and local features with k-nearest neighbor. In addition, the proposed model has achieved 85.72% and 86.16% of accuracy respectively on e-ophtha microaneurysms and DiaretDB1 datasets.

WITHDRAWN: Detection of diabetic retinopathy using partial swarm optimization (PSO) and Gaussian interval type-2 fuzzy membership functions (GIT2FMFS)

Abstract Vision loss due to Diabetic Retinopathy (DR) is an important cause of disability in the population since it affects 3.4 percent of the population. To avoid this problem, we have to detect DR as in early stage. In early stage DR is symptomless; screening is the only way to detect and classify these kinds of diseases. Several automated screening techniques are used to detect DR in retina. Still it took more time. So, our proposed work introduces image processing based soft computing approaches. It will improve the performance of accuracy, sensitivity, specificity and precision and detect the DR in easier and faster manner. This paper presents a novel approach for detection of microaneurysms and Hemorrhages in fundus images. Our process includes following steps i) Pre-Processing ii) Blood Vessel Segmentation iii) Blood Vessel Removal iv) Fovea Localization v) Fovea Elimination vi) Feature Extraction vii) Feature Selection and finally viii) Detection of Diabetic Retinopathy disease such as microaneurysms and hemorrhages. Our experimental results based on MATLAB simulation tool and it take databases of DR2 and MESSIDOR. These databases provide efficient and effective results in classification method.

Automatic microaneurysm detection in fundus image based on local cross-section transformation and multi-feature fusion

Background and objective: Retinal microaneurysm (MA) is one of the earliest clinical signs of diabetic retinopathy(DR). Its detection is essential for controlling DR and preventing vision loss. However, the spatial scale of MA is extremely small and the contrast to surrounding background is subtle, which make MA detection challenging. The purpose of this work is to automatically detect MAs from fundus images. Methods: Our MA detector involves two stages: MA candidate extraction and classification. In MA candidate extraction stage, local minimum region extraction and block filtering are used to exploit the regions where MA may exist. In this way, most of irrelavent background regions are discarded , which subsequently facilitates the training of MA classifier. In the second stage, multiple features are extracted to train the MA classifier. To distinguish MA from vascular regions, we propose a series of descriptors according to the cross-section profile of MA. Specially, as MAs are small and their contrast to surroundings is subtle, we propose local cross-section transformation (LCT) to amplify the difference between the MA and confusing structures. Finally, an under-sampling boosting-based classifier (RUSBoost) is trained to determine whether the candidate is an MA. Results: The proposed method is evaluated on three public available databases i.e. e-ophtha-MA, DiaretDB1 and ROC training set. It achieves high sensitivities for low false positive rates on the three databases. Using the FROC metric, the final scores are 0.516, 0.402 and 0.293 respectively, which are comparable to existing state-of-the-art methods. Conclusions: The proposed local cross-section transformation enhances the discrimination of descriptors by amplifying difference between MAs and confusing structures, which facilitates the classification and improves the detection performances. With the powerful descriptors, our method achieves state-of-the-art performances on three public datasets consistently.

Efficient sampling-based energy function evaluation for ensemble optimization using simulated annealing

In this study, we attempted to develop a method for accelerating parameter optimization of an object detector ensemble over large image datasets by using simulated annealing. We propose a novel sampling-based evaluation method that considers the minimum portion of the dataset required in each iteration to maintain solution quality. This approach can be considered a noisy evaluation of the energy. The sample sizes required during the search process are theoretically determined by adapting the convergence results for noisy evaluation. To determine applicability, we prepared and optimized two ensembles for diabetic retinopathy pre-screening based on microaneurysm detection with convolutional neural network-based and traditional object detectors. Our experimental results indicate that the proposed sampling-based evaluation method substantially reduced the computational time required for optimizing the parameters of the ensembles while preserving solution quality.

Automatic Grading System for Diabetic Retinopathy Diagnosis Using Deep Learning Artificial Intelligence Software

ABSTRACT Purposes: To describe the development and validation of an artificial intelligence–based, deep learning algorithm (DeepDR) for the detection of diabetic retinopathy (DR) in retinal fundus photographs. Methods: Five hundred fundus images, which had detailed labelling of DR lesions, were transmitted to be analysed, including localization of the optic disk and macular, vessel segmentation, detection of lesions, and grading of DR. The multi-level iterative method of convolutional neural network and the strategy of enhanced learning were used to improve the accuracy of the system (DeepDR) for grading DR. Three public data sets were used to further train the software. The final grading results were tested based on the fundus images provided by the hospitals. Results: For 6788 fundus images (both macular and disc centred) of two Hospital Eye Center, the detection of microaneurysm, haemorrhage and hard exudates had an accuracy of 99.7%, 98.4% and 98.1%, respectively. The current algorithm accuracy was 0.96. Another 20,000 fundus images from community screening were selected, and 7593 photos of poor quality were excluded according to quality standards. Accuracy for accurate staging of fundus photos: accuracy was 0.9179. The sensitivity, specificity and area under the curve (AUC) were 80.58%, 95.77% and 0.9327, respectively. Conclusions: This artificial intelligence–based DeepDR can be used with high accuracy for the detection of DR in retinal images. This technology offers the potential to increase the efficiency and accessibility of DR screening programs.

Microaneurysms detection in color fundus images using machine learning based on directional local contrast

BackgroundAs one of the majorcomplications of diabetes, diabetic retinopathy (DR) is a leadingcause of visual impairment and blindness due to delayed diagnosisand intervention. Microaneurysms appear as the earliest symptom ofDR. Accurate and reliable detection of microaneurysms in colorfundus images has great importance for DR screening.MethodsA microaneurysms' detection methodusing machine learning based on directional local contrast (DLC) isproposed for the early diagnosis of DR. First, blood vessels wereenhanced and segmented using improved enhancement function based onanalyzing eigenvalues of Hessian matrix. Next, with blood vesselsexcluded, microaneurysm candidate regions were obtained using shapecharacteristics and connected components analysis. After imagesegmented to patches, the features of each microaneurysm candidatepatch were extracted, and each candidate patch was classified intomicroaneurysm or non-microaneurysm. The main contributions of ourstudy are (1) making use of directional local contrast inmicroaneurysms' detection for the first time, which does make sensefor better microaneurysms' classification. (2) Applying threedifferent machine learning techniques for classification andcomparing their performance for microaneurysms' detection. Theproposed algorithm was trained and tested on e-ophtha MA database,and further tested on another independent DIARETDB1 database.Results of microaneurysms' detection on the two databases wereevaluated on lesion level and compared with existing algorithms.ResultsThe proposed method has achieved better performance compared with existing algorithms on accuracy and computation time. On e-ophtha MA and DIARETDB1 databases, the area under curve (AUC) of receiver operating characteristic (ROC) curve was 0.87 and 0.86, respectively. The free-response ROC (FROC) score on the two databases was 0.374 and 0.210, respectively. The computation time per image with resolution of 2544×1969, 1400×960 and 1500×1152 is 29 s, 3 s and 2.6 s, respectively.ConclusionsThe proposed methodusing machine learning based on directional local contrast of imagepatches can effectively detect microaneurysms in color fundus imagesand provide an effective scientific basis for early clinical DRdiagnosis.

Shape-based filter for micro-aneurysm detection

Automatic detection of micro-aneurysm in color retinal image is important for early screening and diagnosis of diabetic retinopathy. In this paper, a new method is proposed for micro-aneurysm detection based on circular bilateral Gabor filtering. Firstly, a circular bilateral Gabor filter is developed to extract micro-aneurysm candidates. Secondly, false positives are reduced by eliminating small vessels through a process involving local gradient analysis. The proposed method is tested on the retinal images from the Retinopathy Online Challenge database and Tianjin Medical University Metabolic Diseases Hospital. Evaluation results at both image and lesion level demonstrate the efficacy of the proposed method in detecting micro-aneurysm accurately.

True Microaneurysms Detection in Cluttered Retinal Photographs for the Detection of Diabetic Retinopathy

Microaneurysms are the first clinical sign of diabetic retinopathy. The number of microaneurysms is used to indicate the severity of the disease. Early microaneurysm detection can help reduce the incidence of blindness. This paper analyzes the Threshold based technique and Wavelet Decomposition technique to reject specific classes of noises while passing majority of true Microaneurysms using a set of specialized features. The rejection strategy is formulated based on the occurrence frequency and discriminability of the underlying clutter. Threshold based technique separates both classes and further allows various subgroups of clutter class to be handled through a cascade solution. Further a set of morphological and appearance based features are introduced to characterize the clutter and MA structures. This gives flexibility to achieve better result of finding Microaneurysms separately from the clutter classes. These two methods have been analyzed based on Microaneurysms detection. Experimental results shows that, the Threshold based technique reduces the noise in an efficient way and affected Microaneurysms portion is obtained clearly.

Addressing class imbalance in deep learning for small lesion detection on medical images

Deep learning methods utilizing Convolutional Neural Networks (CNNs) have led to dramatic advances in automated understanding of medical images. However, in many medical image classification tasks, lesions occupy only a few pixels of the image. This results in a significant class imbalance between lesion and background. From recent literature, it is known that class imbalance may negatively affect the performance of CNN classification. However, very few research exists in the context of lesion detection. In this work, we propose a two-stage deep learning framework able to deal with the high class imbalance encountered during training of small lesion detectors. First, we train a deep cascade (DC) of long sequences of decision trees with an algorithm designed to handle unbalanced data that also drastically reduces the number of background samples reaching the final stage. The remaining samples are fed to a CNN, whose training benefits from both rebalance and hard mining done by the DC. We evaluated DC-CNN on two severely unbalanced classification problems: microcalcification detection and microaneurysm detection. In both cases, DC-CNN outperformed the CNNs trained with commonly used methods for addressing class imbalance such as oversampling, undersampling, hard mining, cost sensitive learning, and one-class classification. The DC-CNN was also ∼10x faster than CNN at test time.

Automated Microaneurysms Detection and Classification using Multilevel Thresholding and Multilayer Perceptron

The purpose of this paper is to propose an automatic detection of microaneurysms (MAs) in the fundus retina images. In this work, E-optha database of 100 images were utilised to test the performance of the proposed method. The approach covers pre-processing, segmentation, post-processing, feature extraction and classification phases. In pre-processing, the images were filtered and the contrast enhanced. Then, the images were segmented using H-maxima and thresholding technique. Morphological operation was carried out to enhance the images before feature extraction and MAs candidate detection. The detected MAs candidates were classified using multilayer perceptron (MLP). After that, the detected MAs were classified into three classes including background (B), MAs and retinal blood vessels (RBVs). The performances of the classifiers were evaluated in terms of accuracy, sensitivity and specificity. The MLP classifier achieved a better performance than the support vector machine with the highest accuracy of 92.28% under condition 2. This study demonstrated a methodology for automatic detection of MAs using MLP. The proposed methodology successfully classify the MAs, B and RBVs and was reasonably fast to be implemented in real time.

Decrease in the number of microaneurysms in diabetic macular edema after anti-vascular endothelial growth factor therapy: implications for indocyanine green angiography-guided detection of refractory microaneurysms.

We evaluated changes in the numbers of microaneurysms (MAs) on fluorescein angiography (FA) and indocyanine green angiography (IA) in eyes with diabetic macular edema (DME) following intravitreal injection of anti-vascular endothelial growth factor (VEGF) agents. Twenty-one eyes of 16 patients with DME were included in this retrospective study. All patients received an initial loading dose of three monthly injections of anti-VEGF agents; thereafter, they received a pro re nata regimen for at least 12months of follow-up. FA and IA images were obtained before and at 6months after the initial injection. The median numbers of MAs significantly decreased from six (interquartile range [IQR] 3-7) MAs in early-phase FA, three (IQR 3-5) leaky MAs in late-phase FA, and two (IQR 1-4) MAs in late-phase IA at baseline to two (IQR 1-3) MAs in early-phase FA, one (IQR 0-2) leaky MA in late-phase FA, and one (IQR 0-2) MA in late-phase IA at 6months (P < 0.0001 for all). Only the median numbers of MAs in late-phase IA at baseline and at 6months were significantly higher in the recurrent DME group (13 eyes) than in the non-recurrent DME group (five eyes) (three [IQR 2-4] vs one [IQR 1-2], one [IQR 0.5-2] vs zero [P = 0.0185 and P = 0.009]). Intravitreal injection of anti-VEGF agents reduced the numbers of MAs in patients with DME. The numbers of MAs detected by late-phase IA might be useful predictors of DME recurrence.

A novel automated system of discriminating Microaneurysms in fundus images

Diabetic retinopathy, a chronic disease in diabetic patients leads to Vision loss, by disabling microvascular complications, if not recognized and cured at the earlier stage. This article explores a novel and reliable method for automatic early detection of Microaneurysms (MA) in fundus images. Microaneurysms characterized by small red spots on the retina, the red lesions are symptoms of early stage of DR. Development of an automated screening system would assist an ophthalmologist in diagnosing DR at an early stage. Hence, in this paper, a novel feature extraction technique using a Local Neighborhood Differential Coherence Pattern (LNDCP) is proposed. In this method, texture characteristics needed for classification by Feed Forward Neural Network (FFNN) is captured efficiently. The performance of the algorithm is validated using experiments on Retinopathy Online Challenge (ROC) public dataset and a single real-time dataset, AGAR300. Efficiency of the algorithm is benchmarked with state-of-art approaches and a Free-response Receiver Operating Characteristic (FROC) score of 0.481 and 0.442 have been achieved for ROC and AGAR300 respectively.