scholarly journals Automated Detection of Red Lesions Using Superpixel Multichannel Multifeature

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Wei Zhou ◽  
Chengdong Wu ◽  
Dali Chen ◽  
Zhenzhu Wang ◽  
Yugen Yi ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Vessels ◽  
Extraction Method ◽  
Critical Role ◽  
Automatic Detection ◽  
Automated Detection ◽  
Lesion Detection ◽  
Classification Approach ◽  
Contextual Feature

Red lesions can be regarded as one of the earliest lesions in diabetic retinopathy (DR) and automatic detection of red lesions plays a critical role in diabetic retinopathy diagnosis. In this paper, a novel superpixel Multichannel Multifeature (MCMF) classification approach is proposed for red lesion detection. In this paper, firstly, a new candidate extraction method based on superpixel is proposed. Then, these candidates are characterized by multichannel features, as well as the contextual feature. Next, FDA classifier is introduced to classify the red lesions among the candidates. Finally, a postprocessing technique based on multiscale blood vessels detection is modified for removing nonlesions appearing as red. Experiments on publicly available DiaretDB1 database are conducted to verify the effectiveness of our proposed method.

Review of Various Tasks Performed in the Preprocessing Phase of a Diabetic Retinopathy Diagnosis System

2020 ◽  
Vol 16 (4) ◽  
pp. 397-426
Author(s):  
Muhammad Nadeem Ashraf ◽  
Muhammad Hussain ◽  
Zulfiqar Habib
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Vessels ◽  
Early Stage ◽  
Spherical Shape ◽  
Cost Effective ◽  
Lesion Detection ◽  
Diabetic Patients ◽  
Cad System ◽  
Non Invasive ◽  
Optic Discs

Diabetic Retinopathy (DR) is a major cause of blindness in diabetic patients. The increasing population of diabetic patients and difficulty to diagnose it at an early stage are limiting the screening capabilities of manual diagnosis by ophthalmologists. Color fundus images are widely used to detect DR lesions due to their comfortable, cost-effective and non-invasive acquisition procedure. Computer Aided Diagnosis (CAD) of DR based on these images can assist ophthalmologists and help in saving many sight years of diabetic patients. In a CAD system, preprocessing is a crucial phase, which significantly affects its performance. Commonly used preprocessing operations are the enhancement of poor contrast, balancing the illumination imbalance due to the spherical shape of a retina, noise reduction, image resizing to support multi-resolution, color normalization, extraction of a field of view (FOV), etc. Also, the presence of blood vessels and optic discs makes the lesion detection more challenging because these two artifacts exhibit specific attributes, which are similar to those of DR lesions. Preprocessing operations can be broadly divided into three categories: 1) fixing the native defects, 2) segmentation of blood vessels, and 3) localization and segmentation of optic discs. This paper presents a review of the state-of-the-art preprocessing techniques related to three categories of operations, highlighting their significant aspects and limitations. The survey is concluded with the most effective preprocessing methods, which have been shown to improve the accuracy and efficiency of the CAD systems.

Deep Learning Techniques for Diabetic Retinopathy Detection

2020 ◽  
Vol 16 ◽  
Author(s):  
Sehrish Qummar ◽  
Fiaz Gul Khan ◽  
Sajid Shah ◽  
Ahmad Khan ◽  
Ahmad Din ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Blood Vessels ◽  
Blood Sugar ◽  
Automatic Detection ◽  
Multiclass Classification ◽  
The Body ◽  
Future Directions ◽  
Learning Techniques

Diabetes occurs due to the excess of glucose in the blood that may affect many organs of the body. The increase in blood sugar in the body causes many problems. One of the most prominent of these problems is Diabetic Retinopathy (DR). DR occurs due to the mutilation of the blood vessels in a retina. The detection of DR is complicated and time-consuming due to its features for the ophthalmologists. Therefore, automatic detection is required, recently different machine and deep learning techniques are being applied to detect and classify DR. In this paper, we conducted a study of the various techniques available in the literature for the identification/classification of DR, the datasets used, strengths and weaknesses of each method and provides the future directions. Moreover, we also discussed the different steps for the detection that are segmentation of blood vessels in a retina, detecting lesions and other abnormalities of DR in binary and multiclass classification.

scholarly journals Automatic Detection of Blood Vessels in Retinal Images for Diabetic Retinopathy Diagnosis

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
D. Siva Sundhara Raja ◽  
S. Vasuki
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Vessels ◽  
Anisotropic Diffusion ◽  
Vision Loss ◽  
Automatic Detection ◽  
Diabetic Patients ◽  
Morphological Operations ◽  
Anisotropic Diffusion Filter ◽  
Retinal Blood Vessels ◽  
Detection And Diagnosis

Diabetic retinopathy (DR) is a leading cause of vision loss in diabetic patients. DR is mainly caused due to the damage of retinal blood vessels in the diabetic patients. It is essential to detect and segment the retinal blood vessels for DR detection and diagnosis, which prevents earlier vision loss in diabetic patients. The computer aided automatic detection and segmentation of blood vessels through the elimination of optic disc (OD) region in retina are proposed in this paper. The OD region is segmented using anisotropic diffusion filter and subsequentially the retinal blood vessels are detected using mathematical binary morphological operations. The proposed methodology is tested on two different publicly available datasets and achieved 93.99% sensitivity, 98.37% specificity, 98.08% accuracy in DRIVE dataset and 93.6% sensitivity, 98.96% specificity, and 95.94% accuracy in STARE dataset, respectively.

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

2019 ◽  
pp. 361-368
Author(s):  
S. Karthika ◽  
Sandra Johnson
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Vessels ◽  
Automated Detection ◽  
Image Process ◽  
Body Structure ◽  
Diabetic Mellitus ◽  
Visual Disorder ◽  
Open Issue ◽  
Diabetic Population ◽  
Picture Analysis

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.

scholarly journals Diabetic Retinal Grading Using Attention-Based Bilinear Convolutional Neural Network and Complement Cross Entropy

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 816
Author(s):  
Pingping Liu ◽  
Xiaokang Yang ◽  
Baixin Jin ◽  
Qiuzhan Zhou
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Network Model ◽  
Rapid Development ◽  
Image Data ◽  
Lesion Detection ◽  
Great Success

Diabetic retinopathy (DR) is a common complication of diabetes mellitus (DM), and it is necessary to diagnose DR in the early stages of treatment. With the rapid development of convolutional neural networks in the field of image processing, deep learning methods have achieved great success in the field of medical image processing. Various medical lesion detection systems have been proposed to detect fundus lesions. At present, in the image classification process of diabetic retinopathy, the fine-grained properties of the diseased image are ignored and most of the retinopathy image data sets have serious uneven distribution problems, which limits the ability of the network to predict the classification of lesions to a large extent. We propose a new non-homologous bilinear pooling convolutional neural network model and combine it with the attention mechanism to further improve the network’s ability to extract specific features of the image. The experimental results show that, compared with the most popular fundus image classification models, the network model we proposed can greatly improve the prediction accuracy of the network while maintaining computational efficiency.

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