Detection Of Microaneurysms Research Articles

Detection of Mircoaneurysms in Diabetic Retinopathy Using Image Processing and Python

Diabetic retinopathy (DR) is one of the most common reasons for blindness in the working-age population of the world. Diabetic Retinopathy is an eye disease, which occurs with long-standing untreated diabetes. Progression to vision impairment can be slowed down or stopped if DR is detected on time; In detection or screening of DR, automatic methods can play an important role. A microaneurysm is a tiny aneurysm, or swelling, in the side of a blood vessel. People with diabetes, microaneurysms are sometimes found in the retina of the eye. These miniature aneurysms can rupture and leak blood, hence the purpose of our project is to detect the microaneurysms which are the initial stage of Diabetic retinopathy so that we could prevent blindness. An algorithm is designed to detect microaneurysms and tested it on a publicly available DiaretDB1 database, which contains the ground truth for all images. For the detection of Micro aneurysms, the given steps are followed, image acquisition, green channel, and CLAHE, sequential filtering, blood vessel detection, BV extraction from the image, thresholding, and after the microaneurysms will be detected.

Retinopathy Analysis Based on Deep Convolution Neural Network.

At medical checkups or mass screenings, the fundus examination is effective for early detection of systemic hypertension, arteriosclerosis, diabetic retinopathy, etc. In most cases, ophthalmologists and physicians grade retinal images by the condition of the blood vessels, lesions. However, human observation does not provide quantitative results, thus blood vessel analysis is an important process in determining hypertension and arteriosclerosis, quantitatively. This chapter describes the latest automated blood vessel extraction using the deep convolution neural network (DCNN). Diabetic retinopathy is a common cardiovascular disease and a major factor in blindness. Therefore, early detection of diabetic retinopathy is very important to preventing blindness. A microaneurysm is an initial sign of diabetic retinopathy, and much research has been conducted for microaneurysm detection. This chapter also describes diabetic retinopathy detection and automated microaneurysm detection using the DCNN.

A Fast Localization and Extraction of Microaneurysm for Early Detection of Diabetic Retionopathy

Diabetic Retinopathy (DR) is regarded as one of the leading causes of blindness globally. Microaneurysms (MAs) detection is essential to the computer aided diagnosis of DR at an early stage. However, the automatic detection of MAs is still a challenging problem as they are too tiny to be recognized and hard to be distinguished from other similar lesions. Therefore, we propose an efficient localization and extraction method for MAs, where the edge detection and Random Forest are utilized to enhance the accuracy of detection results. Finally, the proposed method is evaluated based on the public retinal image database MESSIDOR. Numerical results show that high accuracy and timely detection can be obtained with the proposed solution.

Improving Microaneurysm Detection from Non-dilated Diabetic Retinopathy Retinal Images using Feature Optimization

Diabetic retinopathy usually does not presents symptoms in an early stage until it gets to a severe stage. An early stage of diabetic retinopathy is associated with the presence of microaneurysms (MAs). The occurrence of blindness can be reduced significantly if MAs are detected. This paper presented an approach to improve automatic MAs detection using feature optimisation. Candidate MAs are detected using mathematic morphological techniques. Originally 20 features are presented. To verify the relevance of all original features, a feature optimisation process is performed. The optimal feature set is searched by a machine learning approach, like naive Bayes and support vector machine classifier. Hand-drawn ground-truth images from expert ophthalmologists are used to measure the performance evaluation. The results showed that the proposed optimal feature set could significantly improve MA detection.

A multi-context CNN ensemble for small lesion detection.

In this paper, we propose a novel method for the detection of small lesions in digital medical images. Our approach is based on a multi-context ensemble of convolutional neural networks (CNNs), aiming at learning different levels of image spatial context and improving detection performance. The main innovation behind the proposed method is the use of multiple-depth CNNs, individually trained on image patches of different dimensions and then combined together. In this way, the final ensemble is able to find and locate abnormalities on the images by exploiting both the local features and the surrounding context of a lesion. Experiments were focused on two well-known medical detection problems that have been recently faced with CNNs: microcalcification detection on full-field digital mammograms and microaneurysm detection on ocular fundus images. To this end, we used two publicly available datasets, INbreast and E-ophtha. Statistically significantly better detection performance were obtained by the proposed ensemble with respect to other approaches in the literature, demonstrating its effectiveness in the detection of small abnormalities.

Automatic Diabetic Retinopathy Screening via Cascaded Framework Based on Image- and Lesion-Level Features Fusion

The early detection of diabetic retinopathy is crucial for preventing blindness. However, it is time-consuming to analyze fundus images manually, especially considering the increasing amount of medical images. In this paper, we propose an automatic diabetic retinopathy screening method using color fundus images. Our approach consists of three main components: edge-guided candidate microaneurysms detection, candidates classification using mixed features, and diabetic retinopathy prediction using fused features of image level and lesion level. We divide a screening task into two sub-classification tasks: 1) verifying candidate microaneurysms by a naive Bayes classifier; 2) predicting diabetic retinopathy using a support vector machine classifier. Our approach can effectively alleviate the imbalanced class distribution problem. We evaluate our method on two public databases: Lariboisiere and Messidor, resulting in an area under the curve of 0.908 on Lariboisiere and 0.832 on Messidor. These scores demonstrate the advantages of our approach over the existing methods.

Detection of Microaneurysms in Fundus Images Based on an Attention Mechanism.

Microaneurysms (MAs) are the earliest detectable diabetic retinopathy (DR) lesions. Thus, the ability to automatically detect MAs is critical for the early diagnosis of DR. However, achieving the accurate and reliable detection of MAs remains a significant challenge due to the size and complexity of retinal fundus images. Therefore, this paper presents a novel MA detection method based on a deep neural network with a multilayer attention mechanism for retinal fundus images. First, a series of equalization operations are performed to improve the quality of the fundus images. Then, based on the attention mechanism, multiple feature layers with obvious target features are fused to achieve preliminary MA detection. Finally, the spatial relationships between MAs and blood vessels are utilized to perform a secondary screening of the preliminary test results to obtain the final MA detection results. We evaluated the method on the IDRiD_VOC dataset, which was collected from the open IDRiD dataset. The results show that our method effectively improves the average accuracy and sensitivity of MA detection.

Microaneurysm Imaging Using Multiple En Face OCT Angiography Image Averaging: Morphology and Visualization

In diabetic retinopathy (DR), OCT angiography (OCTA) could not image all fluorescein angiography (FA)-detected microaneurysms. We investigated whether multiple image averaging could enhance the microaneurysm detection capability of OCTA in patients with DR. Prospective and cross-sectional observational study. Consecutive 31 patients (n= 62 eyes) with DR. All eyes underwent FA and 3× 3 mm fovea-centered OCTA images were obtained using 2 devices: RTVue XR Avanti (Optovue Inc, Fremont, CA) and OCT HS-100 (Canon Inc, Toyko, Japan). OCTA imaging (HS-100) was performed 10 consecutive times. Microaneurysm detection capability was compared among 5 OCTA images (single image,×3,×5, and×10 averaged images and single scan image with the RTVue XR Avanti device). Microaneurysm detection capability and the correlation between microaneurysm clinical characteristics or morphology and the extent of image averaging required for OCTA detection. A total of 415 microaneurysms could be analyzed in 31 eyes from 25 patients. Microaneurysms detected on single image,×3,×5, and×10 averaged OCTA images were 144 (34.7%), 227 (54.7%), 285 (68.7%), and 306 (73.7%), respectively. Microaneurysm detection capability was significantly increased with increased image averaging. Microaneurysm detection with OCTA was not correlated with retinal thickness, FA leakiness, and indocyanine green angiogram detection or the number of averaged images, whereas there was significant correlation between microaneurysm morphology and microaneurysm visibility by the image-averaging process for 4 morphologies, particular the focal bulge types (P < 0.01). In DR, multiple image averaging is useful for increasing the microaneurysm detection capability of OCTA, especially for focal bulge-type microaneurysms.

Discrimination of microaneurysm in color retinal images using texture descriptors

The occurrence of diabetic retinopathy and diabetic mellitus has been increasing worldwide. Currently, ophthalmologists face a lot of challenges in identifying various stages in diabetic retinopathy. Among these stages, the early stage is microaneurysm. A new computer-aided microaneurysm detection system based on texture features is presented. The histogram of texture descriptor reveals the texture characteristics of each pixel which effectively increases the accuracy of MA detection than shape-based features. The extracted features using Local Binary Pattern contribute more while discriminating the lesions using support vector machine classifier. Validations based on free-response receiver operating characteristic score and area under curve are obtained for ROC, MESSIDOR and DIARETDB1 dataset. The ability to process images with different intensities and less computation time guarantees the robustness of this system.

Microaneurysm detection in fundus images using a two-step convolutional neural network

Background and objectivesDiabetic retinopathy (DR) is the leading cause of blindness worldwide, and therefore its early detection is important in order to reduce disease-related eye injuries. DR is diagnosed by inspecting fundus images. Since microaneurysms (MA) are one of the main symptoms of the disease, distinguishing this complication within the fundus images facilitates early DR detection. In this paper, an automatic analysis of retinal images using convolutional neural network (CNN) is presented.MethodsOur method incorporates a novel technique utilizing a two-stage process with two online datasets which results in accurate detection while solving the imbalance data problem and decreasing training time in comparison with previous studies. We have implemented our proposed CNNs using the Keras library.ResultsIn order to evaluate our proposed method, an experiment was conducted on two standard publicly available datasets, i.e., Retinopathy Online Challenge dataset and E-Ophtha-MA dataset. Our results demonstrated a promising sensitivity value of about 0.8 for an average of >6 false positives per image, which is competitive with state of the art approaches.ConclusionOur method indicates significant improvement in MA-detection using retinal fundus images for monitoring diabetic retinopathy.

Automated detection of microaneurysms using Stockwell transform and statistical features

Microaneurysms (MAs) are the earliest pre-eminent indicators of diabetic retinopathy (DR) and are hard to distinguish for ophthalmologists on standard fundus images. This study proposes a method based on discrete orthonormal Stockwell transform and statistical features for discriminating between normal and diseased retinal images. Feature extraction by the two different approaches is consolidated and a total of 24 features are extracted for classifier models. Training and testing of the proposed method have been accomplished using 1140 retinal colour photographs. A comparative study using eight best-known classifiers is showcased for detection of MAs and the performance of the classifiers is evaluated using retinal images by performing ten-fold cross-validation procedure. Simulation results demonstrate the efficiency and adequacy of the proposed method which mainly characterise the textural features. The proposed method is compared with existing algorithms and the results show that the algorithm detects DR with high veracity. With the high accuracy and positive prediction, the proposed system assures promising results in early diagnosis of DR.

Detection of microaneurysms using ant colony algorithm in the early diagnosis of diabetic retinopathy

Microaneurysms are lesions in the shape of small circular dilations which result from thinning in peripheral retinal blood vessels due to diabetes and increasing intra-retinal blood pressure. Because it is considered as the most important clinical finding in the diagnosis of diabetic retinopathy, accurate detection of these lesions bear utmost importance in the early diagnosis of diabetic retinopathy. The present study aims to accurately, effectively and automatically detect microaneurysms which are difficult to detect in color fundus images in early stage. To this aim, ant colony algorithm, which is an important optimization method, was used instead of conventional image processing techniques. First, retinal vascular structure was extracted from color fundus images in Messidor and DiaretDB1 data sets. Afterwards, the segmentation of microaneurysms was effectively carried out using ant colony algorithm. The same procedure was also applied to five different image processing and clustering algorithms (watershed, random walker, k-means, maximum entropy and region growing) in order to compare the performance of the proposed method with other methods. Microaneurysm images manually detected by a specialist eye doctor were used to measure the performances of above-mentioned methods. The similarities among microaneurysms which were automatically and manually segmented were tested using Dice and Jaccard similarity index values. Dice index values obtained from the study vary between 0.52 and 0.98 in maximum entropy, 0.55 and 0.88 in watershed, 0.75 and 0.86 in region growing, 0.55 and 0.78 in k-means, and 0.66 and 0.83 in random walker, and 0.81 and 0.9 in ant colony. Similar performance values were also obtained in Jaccard index. The results show that different performances were observed in the conventional segmentation of microaneurysms depending on the image quality. On the other hand, the ant colony based method proposed in this paper displays a more stabilized and higher performance irrespective of image contrast. Therefore, it is evident that the proposed method successfully detects microaneurysms even in low quality images, thus helping specialists diagnose them in an easier way.

Survey on Convolutional Neural Network Based Efficient Automated Detection of Micro Aneurysm in Diabetic Retinopathy

Diabetic Retinopathy (DR) is that the most typical explanation for visual disorder of the attention depends upon polygenic disorder. For this reason, early detection of diabetic retinopathy is of crucial importance. The primary sign of diabetic retinopathy within the membrane is that the presence of the micro aneurysms (MAs) that cause due to injury within the membrane as a long abnormality impact results in diabetic mellitus. Despite many makes an attempt, automated detection of micro aneurysm from digital body structure pictures still remains to be associate open downside. Early identification of the micro aneurysms (MAs) helps us to cut back and forestall diabetic retinopathy at the first stage. Diabetic Retinopathy (DR) could be a complication of polygenic disorder and a number one explanation for visual disorder within the world. It happens once polygenic disorder damages the little blood vessels within the membrane. If the blood vessels within the membrane get harm they develop a balloon like swelling referred to as micro aneurysms. The detection of micro aneurysms (MAs) in color body structure pictures remains associate open issue within the medical image process because of the low availableness of reliability. The most two sorts of diabetic retinopathy are Non-Proliferate Diabetic Retinopathy (NPDR) and Proliferate Diabetic Retinopathy (PDR). Picture analysis by trained people, which may be an awfully pricey and time intense task because of the massive diabetic population.

Mathematical morphology for microaneurysm detection in fundus images.

Fundus image analysis is the basis for the better understanding of retinal diseases which are found due to diabetes. Detection of earlier markers such as microaneurysms that appear in fundus images combined with treatment proves beneficial to prevent further complications of diabetic retinopathy with an increased risk of sight loss. The proposed algorithm consists of three modules: (1) image enhancement through morphological processing; (2) the extraction and removal of red structures, such as blood vessels preceded by detection and removal of bright artefacts; (3) finally, the true microaneurysm candidate selection among other structures based on feature extraction set. The proposed strategy is successfully evaluated on two publicly available databases containing both normal and pathological images. The sensitivity of 89.22%, specificity of 91% and accuracy of 92% achieved for the detection of microaneurysms for Diaretdb1 database images. The algorithm evaluation for microaneurysm detection has a sensitivity of 83% and specificity 82% for e-ophtha database. In automated detection system, the successful detection of the number of microaneurysms correlates with the stages of the retinal diseases and its early diagnosis. The results for true microaneurysm detection indicates it as a useful tool for screening colour fundus images, which proves time saving for counting of microaneurysms to follow Diabetic Retinopathy Grading Criteria.

Automatic Detection of Microaneurysms in Diabetic Retinopathy Using Python

This paper discusses the detection of microaneurysms(MAs) from colour fundus images using image processing. In this paper, we present a web application to detect MAs from fundus image more efficiently and on real-time purpose. This paper includes a unique method to detect and mask the optic disc (OD) and blob detection to detect MAs. To examine the accuracy of the system, we processed 63 colour fundus images and got an accuracy for 95% accuracy for optic disc elimination and 56% accuracy for microaneurysms detection.

GLCM-based detection and classification of microaneurysm in diabetic retinopathy fundus images

GLCM-based detection and classification of microaneurysm in diabetic retinopathy fundus images

GLCM-based detection and classification of microaneurysm in diabetic retinopathy fundus images

Diabetic retinopathy is a major cause of blindness and it includes the lesions like microaneurysms, haemorrhages, and exudates. Microaneurysms are the first clinical sign of diabetic retinopathy and it is a small red dot on the retinopathy fundus images. The number of micro aneurysms is used to indicate the severity of the disease. The proposed algorithm detects and classifies the micro aneurysm from diabetic retinopathy fundus images in low resolution images also. Initially the image is processed by a median filter and enhanced by contrast limited adaptive histogram equalisation (CLAHE). Micro aneurysms are detected by extended minima method for candidate extraction. The statistical features are extracted by grey level coocurrence matrix (GLCM) and are given to the classifier to classify microaneurysms accurately. These detected MA are validated by comparing with expert ophthalmologists' hand-drawn ground-truth images. The simulation results show the performance of the proposed algorithm.

Analysis on Image Processing Technology-based Diabetic Retinopathy

Diabetic retinopathy is a dangerous eye disease which causes the blindness widely in the human society. It arises due to high sugar level in the blood. The eye disease caused due to the diabetes is called diabetic retinopathy. The symptoms of diabetic retinopathy are red lesions such as microaneurysms (MA), intraretinal hemorrhages and bright lesions such as exudates, cotton wool spots and blood vessels. Microaneurysm (MA) is one of the features of the diabetic retinopathy. They are discrete, localized saccular distensions of the weakened capillary walls and appear as small round dark red dots on the retinal surface. According to the medical definition of MA, it is a reddish, circular pattern with a diameter λ is less than 125μm. Microaneurysms are mostly found near thin blood vessels, but cannot actually be located on the blood vessels. According to the number of microaneurysms, the diabetic retinopathy can be classified as mild stage, moderate stage and the severe stage. To detect the microaneurysms, the optic disc and the blood vessels are firstly detected because they are normal features of the image. And also, in the detection of microaneurysms the pre-processing stage is very important. In the pre-processing stage, the filtering techniques and the histogram equalization techniques are applied to reduce the effect of noise and uneven illumination cases. To detect the optic disc, blood vessels and the microaneurysms, the mathematical morphological method is applied. The result of this research can help in the screening of diabetic retinopathy.

Fundus images analysis using deep features for detection of exudates, hemorrhages and microaneurysms

BackgroundConvolution neural networks have been considered for automatic analysis of fundus images to detect signs of diabetic retinopathy but suffer from low sensitivity.MethodsThis study has proposed an alternate method using probabilistic output from Convolution neural network to automatically and simultaneously detect exudates, hemorrhages and microaneurysms. The method was evaluated using two approaches: patch and image-based analysis of the fundus images on two public databases: DIARETDB1 and e-Ophtha. The novelty of the proposed method is that the images were analyzed using probability maps generated by score values of the softmax layer instead of the use of the binary output.ResultsThe sensitivity of the proposed approach was 0.96, 0.84 and 0.85 for detection of exudates, hemorrhages and microaneurysms, respectively when considering patch-based analysis. The results show overall accuracy for DIARETDB1 was 97.3% and 86.6% for e-Ophtha. The error rate for image-based analysis was also significantly reduced when compared with other works.ConclusionThe proposed method provides the framework for convolution neural network-based analysis of fundus images to identify exudates, hemorrhages, and microaneurysms. It obtained accuracy and sensitivity which were significantly better than the reported studies and makes it suitable for automatic diabetic retinopathy signs detection.

Retinal Microaneurysms Detection Using Local Convergence Index Features.

Retinal microaneurysms (MAs) are the earliest clinical sign of diabetic retinopathy disease. Detection of MAs is crucial for the early diagnosis of diabetic retinopathy and prevention of blindness. In this paper, a novel and reliable method for automatic detection of MAs in retinal images is proposed. In the first stage of the proposed method, several preliminary microaneurysm candidates are extracted using a gradient weighting technique and an iterative thresholding approach. In the next stage, in addition to intensity and shape descriptors, a new set of features based on local convergence index filters is extracted for each candidate. Finally, the collective set of features is fed to a hybrid sampling/boosting classifier to discriminate the MAs from non-MAs candidates. The method is evaluated on images with different resolutions and modalities (color and scanning laser ophthalmoscope) using six publicly available data sets including the retinopathy online challenges (ROC) data set. The proposed method achieves an average sensitivity score of 0.471 on the ROC data set outperforming state-of-the-art approaches in an extensive comparison. The experimental results on the other five data sets demonstrate the effectiveness and robustness of the proposed MAs detection method regardless of different image resolutions and modalities.