Detection Of Microaneurysms Research Articles

Automatic detection of microaneurysms in fundus images based on multiple preprocessing fusion to extract features

Diabetic retinopathy (DR) is the main cause of new cases of blindness, and retinal microaneurysm (MA) is one of the early clinical manifestations of DR. However, it is difficult to directly observe MA with naked eyes because of their small size and poor contrast with the surrounding background. This paper presents a new method for automatic detection of MAs from fundus images. It mainly includes four steps: image preprocessing, candidate region extraction, feature extraction and classification. At the stage of feature extraction, different from traditional methods, we propose a feature extraction method based on multi-preprocessing fusion. In this paper, features are extracted from the original green channel image and the original gray image respectively, and then different processing methods are used to extract the same features from multiple pre-processed images. Finally, the extracted features are classified by multi-layer perceptron (MLP) classifier to determine whether the candidate object is microaneurysm (MA). The method is evaluated on e-ophtha-MA database and achieved high sensitivity. Using the FROC (free-response ROC) indicator, the detection achieves the F-score, accuracy, sensitivity, specificity and AUC (area under the curve) of 0.507, 0.940, 0.870, 0.938 and 0.9821, respectively, compared to the most advanced methods available. The proposed method of multi-preprocessing and fusion feature extraction is beneficial to classification and improves the detection performance.

Automatic Detection of Microaneurysms in Fundus Images Using an Ensemble-Based Segmentation Method.

In this study, a novel method for automatic microaneurysm detection in color fundus images is presented. The proposed method is based on three main steps: (1) image breakdown to smaller image patches, (2) inference to segmentation models, and (3) reconstruction of the predicted segmentation map from output patches. The proposed segmentation method is based on an ensemble of three individual deep networks, such as U-Net, ResNet34-UNet and UNet++. The performance evaluation is based on the calculation of the Dice score and IoU values. The ensemble-based model achieved higher Dice score (0.95) and IoU (0.91) values compared to other network architectures. The proposed ensemble-based model demonstrates the high practical application potential for detection of early-stage diabetic retinopathy in color fundus images.

Microaneurysms detection in retinal fundus images based on shape constraint with region-context features

Microaneurysms (MAs) are the earliest lesions diabetic retinopathy (DR), and the accurate MA detection can assist in early diagnosis of diabetes. Many effective detection techniques have been proposed recently, the interference of blood vessels and blurry boundaries of MAs reduce the performance of these methods. To address these problems, this paper proposes an accurate MA detection method. In the candidate detection stage, a shape suppression filter is proposed to remove strip-shaped blood vessels and retain the dark blobs. In order to enhance blurry boundaries of MAs, we apply the additive bias correction level set to locate circular regions. In the next stage of feature extraction, a new set of features based on gray level co-occurrence matrix (GLCM) is extracted for each candidate to discriminate the MAs from non-MAs candidates using the random undersampling boosting (RUSBoost) classifier. We test our method on four public datasets, resulting in the optimal performance to the existing methods. It achieves average sensitivity values of 0.672, 0.721, 0.602 and 0.735, respectively.

Detection and diabetic retinopathy grading using digital retinal images

Diabetic Retinopathy is an eye disorder that affects people suffering from diabetes. Higher sugar levels in blood leads to damage of blood vessels in eyes and may even cause blindness. Diabetic retinopathy is identified by red spots known as microanuerysms and bright yellow lesions called exudates. It has been observed that early detection of exudates and microaneurysms may save the patient’s vision and this paper proposes a simple and effective technique for diabetic retinopathy. Both publicly available and real time datasets of colored images captured by fundus camera have been used for the empirical analysis. In the proposed work, grading has been done to know the severity of diabetic retinopathy i.e. whether it is mild, moderate or severe using exudates and micro aneurysms in the fundus images. An automated approach that uses image processing, features extraction and machine learning models to predict accurately the presence of the exudates and micro aneurysms which can be used for grading has been proposed. The research is carried out in two segments; one for exudates and another for micro aneurysms. The grading via exudates is done based upon their distance from macula whereas grading via micro aneurysms is done by calculating their count. For grading using exudates, support vector machine and K-Nearest neighbor show the highest accuracy of 92.1% and for grading using micro aneurysms, decision tree shows the highest accuracy of 99.9% in prediction of severity levels of the disease.

Improved Faster-RCNN Based on Multi Feature Scale Fusion for Automatic Detection of Microaneurysms in Retina

Improved Faster-RCNN Based on Multi Feature Scale Fusion for Automatic Detection of Microaneurysms in Retina

Microaneurysms detection in retinal images using a multi-scale approach

One of the earliest signs of diabetic retinopathy is the appearance of small and round red dots in retinal fundus images, known as microaneurysms. Microaneurysms automatic detection is a challenging task due to their small and subtle nature. In this paper, a new multi-scale algorithm developed for the automatic detection of microaneurysms in retinal fundus images is proposed. The proposed method is divided into three stages, namely, image pre-processing, microaneurysm candidates detection and retinal vessels detection, and labelling of the microaneurysm candidates. Apart the initial image pre-processing, all stages of the algorithm are designed using a multi-scale framework, providing the mechanism for the effective microaneurysms detection. Four publicly available databases are used for performance evaluation. The proposed method proves to be competitive against other state-of-the-art approaches in microaneurysms detection and diabetic retinopathy detection.

Detection of microaneurysms in fundus images based on improved YOLOv4 with SENet embedded

Microaneurysm is the initial symptom of diabetic retinopathy. Eliminating this lesion can effectively prevent diabetic retinopathy in the early stage. However, due to the complex retinal structure and the different brightness and contrast of fundus image because of different factors such as patients, environment and acquisition equipment, the existing detection algorithms are difficult to achieve the accurate detection and location of the lesion. Therefore, an improved detection algorithm of you only look once (YOLO) v4 with Squeeze-and-Excitation networks (SENet) embedded was proposed. Firstly, an improved and fast fuzzy c-means clustering algorithm was used to optimize the anchor parameters of the target samples to improve the matching degree between the anchors and the feature graphs; Then, the SENet attention module was embedded in the backbone network to enhance the key information of the image and suppress the background information of the image, so as to improve the confidence of microaneurysms; In addition, an spatial pyramid pooling was added to the network neck to enhance the acceptance domain of the output characteristics of the backbone network, so as to help separate important context information; Finally, the model was verified on the Kaggle diabetic retinopathy dataset and compared with other methods. The experimental results showed that compared with other YOLOv4 network models with various structures, the improved YOLOv4 network model could significantly improve the automatic detection results such as F-score which increased by 12.68%; Compared with other network models and methods, the automatic detection accuracy of the improved YOLOv4 network model with SENet embedded was obviously better, and accurate positioning could be realized. Therefore, the proposed YOLOv4 algorithm with SENet embedded has better performance, and can accurately and effectively detect and locate microaneurysms in fundus images.

Automatic detection of microaneurysms in optical coherence tomography images of retina using convolutional neural networks and transfer learning

Microaneurysms (MAs) are pathognomonic signs that help clinicians to detect diabetic retinopathy (DR) in the early stages. Automatic detection of MA in retinal images is an active area of research due to its application in screening processes for DR which is one of the main reasons of blindness amongst the working-age population. The focus of these works is on the automatic detection of MAs in en face retinal images like fundus color and Fluorescein Angiography (FA). On the other hand, detection of MAs from Optical Coherence Tomography (OCT) images has 2 main advantages: first, OCT is a non-invasive imaging technique that does not require injection, therefore is safer. Secondly, because of the proven application of OCT in detection of Age-Related Macular Degeneration, Diabetic Macular Edema, and normal cases, thanks to detecting MAs in OCT, extensive information is obtained by using this imaging technique. In this research, the concentration is on the diagnosis of MAs using deep learning in the OCT images which represent in-depth structure of retinal layers. To this end, OCT B-scans should be divided into strips and MA patterns should be searched in the resulted strips. Since we need a dataset comprising OCT image strips with suitable labels and such large labelled datasets are not yet available, we have created it. For this purpose, an exact registration method is utilized to align OCT images with FA photographs. Then, with the help of corresponding FA images, OCT image strips are created from OCT B-scans in four labels, namely MA, normal, abnormal, and vessel. Once the dataset of image strips is prepared, a stacked generalization (stacking) ensemble of four fine-tuned, pre-trained convolutional neural networks is trained to classify the strips of OCT images into the mentioned classes. FA images are used once to create OCT strips for training process and they are no longer needed for subsequent steps. Once the stacking ensemble model is obtained, it will be used to classify the OCT strips in the test process. The results demonstrate that the proposed framework classifies overall OCT image strips and OCT strips containing MAs with accuracy scores of 0.982 and 0.987, respectively.

Automatic Detection of Microaneurysms in OCT Images Using Bag of Features.

Diabetic retinopathy (DR) caused by diabetes occurs as a result of changes in the retinal vessels and causes visual impairment. Microaneurysms (MAs) are the early clinical signs of DR, whose timely diagnosis can help detecting DR in the early stages of its development. It has been observed that MAs are more common in the inner retinal layers compared to the outer retinal layers in eyes suffering from DR. Optical coherence tomography (OCT) is a noninvasive imaging technique that provides a cross-sectional view of the retina, and it has been used in recent years to diagnose many eye diseases. As a result, this paper attempts to identify areas with MA from normal areas of the retina using OCT images. This work is done using the dataset collected from FA and OCT images of 20 patients with DR. In this regard, firstly fluorescein angiography (FA) and OCT images were registered. Then, the MA and normal areas were separated, and the features of each of these areas were extracted using the Bag of Features (BOF) approach with the Speeded-Up Robust Feature (SURF) descriptor. Finally, the classification process was performed using a multilayer perceptron network. For each of the criteria of accuracy, sensitivity, specificity, and precision, the obtained results were 96.33%, 97.33%, 95.4%, and 95.28%, respectively. Utilizing OCT images to detect MAs automatically is a new idea, and the results obtained as preliminary research in this field are promising.

Performance analysis of multi-level thresholding for microaneurysm detection

Performance analysis of multi-level thresholding for microaneurysm detection

Detection of microaneurysms in color fundus images based on local Fourier transform

Retinal microaneurysm (MA) is one of the early clinical signs of diabetic retinopathy (DR), which is essential for the early diagnosis of DR. Its shape is often shown as a red dot, which makes detection of MA challenging because of its tiny size. There are many effective MA automatic detection methods having been proposed, but these methods are hard to describe the overall shape of Mas. This paper proposes an automatic MA detection method based on local Fourier transform which splits the original image into small independent images and calculates their local Fourier transforms in isolation. After that, the object is not the whole image but a series of independent small images. We also extract a statistical feature to check for normality and design a new feature to represent the difference among every direction. After that, a group of candidate points are extracted from the images. Finally, some features are extracted from the candidate points, and the confidence scores of the candidate points are obtained by using random forest algorithm for classification. The proposed method obtained a FROC curve score of 0.847 on the E-Ophtha MA database, which is better than most existing methods. In the ROC database, although the FROC curve score is only 0.283, the AUC can reach 0.961, which is better than most methods. It can be seen that the proposed method can achieve a better result with low noise and large sample size.

Hybrid multi-kernel SVM algorithm for detection of microaneurysm in color fundus images.

Diabetic retinopathy (DR) is a chronic disease that may cause vision loss in diabetic patients. Microaneurysms which are characterized by small red spots on the retina due to fluid or blood leakage from the weak capillary wall often occur during the early stage of DR, making screening at this stage is essential. In this paper, an automatic screening system for early detection of DR in retinal images is developed using a combined shape and texture features. Due to minimum number of hand-crafted features, the computational burden is much reduced. The proposed hybrid multi-kernel support vector machine classifier is constructed by learning a kernel model formed as a combination of the base kernels. This approach outperforms the recent deep learning techniques in terms of the evaluation metrics. The efficiency of the proposed scheme is experimentally validated on three public datasets - Retinopathy Online Challenge, DIARETdB1, MESSIDOR, and AGAR300 (developed for this study). Studies reveal that the proposed model produced the best results of 0.503 in ROC dataset, 0.481 in DIARETdB1, and 0.464 in the MESSIDOR dataset in terms of FROC score. The AGAR300 database outperforms the existing MA detection algorithm in terms of FROC, AUC, F1 score, precision, sensitivity, and specificity which guarantees the robustness of this system.

Microaneurysms Detection Using Grey Wolf Optimizer and Modified K-Nearest Neighbor for Early Diagnosis of Diabetic Retinopathy

Microaneurysms Detection Using Grey Wolf Optimizer and Modified K-Nearest Neighbor for Early Diagnosis of Diabetic Retinopathy

Automatic Microaneurysms Detection for Early Diagnosis of Diabetic Retinopathy Using Improved Discrete Particle Swarm Optimization.

Diabetic retinopathy (DR) is one of the most important microvascular complications associated with diabetes mellitus. The early signs of DR are microaneurysms, which can lead to complete vision loss. The detection of DR at an early stage can help to avoid non-reversible blindness. To do this, we incorporated fuzzy logic techniques into digital image processing to conduct effective detection. The digital fundus images were segmented using particle swarm optimization to identify microaneurysms. The particle swarm optimization clustering combined the membership functions by grouping the high similarity data into clusters. Model testing was conducted on the publicly available dataset called DIARETDB0, and image segmentation was done by probability-based (PBPSO) clustering algorithms. Different fuzzy models were applied and the outcomes were compared with our probability discrete particle swarm optimization algorithm. The results revealed that the proposed PSO algorithm achieved an accuracy of 99.9% in the early detection of DR.

Retinal microaneurysm detection based on transformation splicing and multi-context ensemble learning

Retinal microaneurysm (MA) detection is essential for diagnosis of diabetic retinopathy (DR) by providing the earliest clinical sign of DR. However, automatically detecting MA has always been a challenge due to the extremely small proportion of MA, the susceptibility to interference from blood vessels, and the obvious contrast difference between MAs. This paper proposed a novel deep learning method to achieve accurate MA detection based on transformation splicing (TS) and multi-context ensemble learning. TS rebalances the proportion of MA and reduces interference from blood vessels by transforming the pixel distribution of each candidate image and reinforcing the features of difficult samples, which enables the subsequent model to better learn the enhanced image features. At the same time, a multi-context ensemble learning combining dual deep learning models and attention mechanism is designed to adaptively learn different spliced image contexts, which improves detection performance for weak MAs. The final scores of the proposed method in e-ophatha-MA, DiaretDB1 and ROC three public datasets are 0.518, 0.429 and 0.306 respectively, which demonstrates the state-of-the-art performance for MA detection.

Deep Learning Approach for Automatic Microaneurysms Detection.

In diabetic retinopathy (DR), the early signs that may lead the eyesight towards complete vision loss are considered as microaneurysms (MAs). The shape of these MAs is almost circular, and they have a darkish color and are tiny in size, which means they may be missed by manual analysis of ophthalmologists. In this case, accurate early detection of microaneurysms is helpful to cure DR before non-reversible blindness. In the proposed method, early detection of MAs is performed using a hybrid feature embedding approach of pre-trained CNN models, named as VGG-19 and Inception-v3. The performance of the proposed approach was evaluated using publicly available datasets, namely “E-Ophtha” and “DIARETDB1”, and achieved 96% and 94% classification accuracy, respectively. Furthermore, the developed approach outperformed the state-of-the-art approaches in terms of sensitivity and specificity for microaneurysms detection.

Local Structure Awareness-Based Retinal Microaneurysm Detection with Multi-Feature Combination.

Retinal microaneurysm (MA) is the initial symptom of diabetic retinopathy (DR). The automatic detection of MA is helpful to assist doctors in diagnosis and treatment. Previous algorithms focused on the features of the target itself; however, the local structural features of the target and background are also worth exploring. To achieve MA detection, an efficient local structure awareness-based retinal MA detection with the multi-feature combination (LSAMFC) is proposed in this paper. We propose a novel local structure feature called a ring gradient descriptor (RGD) to describe the structural differences between an object and its surrounding area. Then, a combination of RGD with the salience and texture features is used by a Gradient Boosting Decision Tree (GBDT) for candidate classification. We evaluate our algorithm on two public datasets, i.e., the e-ophtha MA dataset and retinopathy online challenge (ROC) dataset. The experimental results show that the performance of the trained model significantly improved after combining traditional features with RGD, and the area under the receiver operating characteristic curve (AUC) values in the test results of the datasets e-ophtha MA and ROC increased from 0.9615 to 0.9751 and from 0.9066 to 0.9409, respectively.

Retinal microaneurysms detection using adversarial pre-training with unlabeled multimodal images

The detection of retinal microaneurysms is crucial for the early detection of important diseases such as diabetic retinopathy. However, the detection of these lesions in retinography, the most widely available retinal imaging modality, remains a very challenging task. This is mainly due to the tiny size and low contrast of the microaneurysms in the images. Consequently, the automated detection of microaneurysms usually relies on extensive ad-hoc processing. In this regard, although microaneurysms can be more easily detected using fluorescein angiography, this alternative imaging modality is invasive and not adequate for regular preventive screening.In this work, we propose a novel deep learning methodology that takes advantage of unlabeled multimodal image pairs for improving the detection of microaneurysms in retinography. In particular, we propose a novel adversarial multimodal pre-training consisting in the prediction of fluorescein angiography from retinography using generative adversarial networks. This pre-training allows learning about the retina and the microaneurysms without any manually annotated data. Additionally, we also propose to approach the microaneurysms detection as a heatmap regression, which allows an efficient detection and precise localization of multiple microaneurysms. To validate and analyze the proposed methodology, we perform an exhaustive experimentation on different public datasets. Additionally, we provide relevant comparisons against different state-of-the-art approaches. The results show a satisfactory performance of the proposal, achieving an Average Precision of 64.90%, 31.36%, and 33.55% in the E-Ophtha, ROC, and DDR public datasets. Overall, the proposed approach outperforms existing deep learning alternatives while providing a more straightforward detection method that can be effectively applied to raw unprocessed retinal images.

Comparison of multicolor scanning laser ophthalmoscopy and optical coherence tomography angiography for detection of microaneurysms\xa0in diabetic retinopathy

This study aimed to evaluate the usefulness of multicolor (MC) scanning laser ophthalmoscopy (MC-SLO) in detecting microaneurysm (MA) in eyes with diabetic retinopathy (DR). This was a retrospective cross-sectional study. Eyes with DR underwent fluorescein angiography (FA), MC-SLO, optical coherence tomography angiography (OCTA), and color fundus photography (CFP) were analyzed. The foveal region was cut in an 6 × 6 mm image and the number of MA in each image was counted by retina specialists to determine the sensitivity and positive predictive value. FA results were used as the ground standard. MAs were classified as those with early, late, or no dye leakage based on FA images. Fifty-four eyes of 35 patients with an average age of 64.5 ± 1.24 years were included. The sensitivity of MA detection was 37.3%, 15.3%, and 4.12% in MC-SLO, OCTA, and CFP, respectively (P < 0.01 in each pair).The positive predictive value was 66.4%, 46.4%, and 27.6% in MC, OCTA, and CFP, respectively (P < 0.01 in each pair). Sensitivity for MAs with early leakage was 36.4% in MC-SLO, which was significantly higher than 4.02% in OCTA. MC-SLO was more useful in detecting MA in eyes with DR than OCTA.

A multi-scale segmentation-to-classification network for tiny microaneurysm detection in fundus images

Microaneurysms (MAs) are recognized as the earliest indicators of Diabetic retinopathy (DR), which play an important role in DR screening. However, the detection of MAs is full of challenges due to the fact that the MAs are tiny spots in the complex situations. In this paper, we propose a multi-scale segmentation-to-classification model to improve the accuracy of d MAs detection in the complex situations. This proposed model is primarily composed of two stages: (1) the segmentation of MAs and; (2) the classification for MAs. In the segmentation stage, a multi-scale residual network, termed as MSRNet, is designed to explore the necessary information from the MAs with large size variance. In the classification stage, a multi-scale efficient network, named as MS-EfficientNet, is built to discriminate the false MAs from the MA candidates detected by the MSRNet. Furthermore, we reasonably expand the positive patches by adding constrained variances to the MA ground truths. Experiments show that the proposed model significantly outperforms the state-of-the-art with an average sensitivity score of 0.715 on the E-ophtha-MA datasets. The further results with other datasets also demonstrate that the proposed model for MAs segmentation is highly effective and robust. Our source code will be available on the GitHub: https://github.com/Lyblue11/Microaneurysm-Detection.