Automatic detection of exudates in retinal images based on threshold moving average models

Wisaeng, Kittipol; Hiransakolwong, Nualsawat; Pothiruk, Ekkarat

doi:10.1134/s0006350915020220

Cited by 12 publications

(5 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For hard exudates detection, [6] presents a median filter-based object segmentation and dynamic thresholding-based image processing approach. Reference [7] presents a detection strategy for brilliant exudates, and [8] The assessments are performed using images from the datasets E-Ophtha [9] and DIARETDB1 [10], and classification accuracy and time efficiency are measured.…”

Section: Soft Exudates 2 Encircled Plaques Of Exudates 3 Hard Exudatesmentioning

confidence: 99%

IM-EDRD from Retinal Fundus Images Using Multi-Level Classification Techniques

Karthikeyan¹,

Anita²

2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

In recent years, there has been a significant increase in the number of people suffering from eye illnesses, which should be treated as soon as possible in order to avoid blindness. Retinal Fundus images are employed for this purpose, as well as for analysing eye abnormalities and diagnosing eye illnesses. Exudates can be recognised as bright lesions in fundus pictures, which can be the first indicator of diabetic retinopathy. With that in mind, the purpose of this work is to create an Integrated Model for Exudate and Diabetic Retinopathy Diagnosis (IM-EDRD) with multi-level classifications. The model uses Support Vector Machine (SVM)-based classification to separate normal and abnormal fundus images at the first level. The input pictures for SVM are pre-processed with Green Channel Extraction and the retrieved features are based on Gray Level Co-occurrence Matrix (GLCM). Furthermore, the presence of Exudate and Diabetic Retinopathy (DR) in fundus images is detected using the Adaptive Neuro Fuzzy Inference System (ANFIS) classifier at the second level of classification. Exudate detection, blood vessel extraction, and Optic Disc (OD) detection are all processed to achieve suitable results. Furthermore, the second level processing comprises Morphological Component Analysis (MCA) based image enhancement and object segmentation processes, as well as feature extraction for training the ANFIS classifier, to reliably diagnose DR. Furthermore, the findings reveal that the proposed model surpasses existing models in terms of accuracy, time efficiency, and precision rate with the lowest possible error rate.

show abstract

Section: Soft Exudates 2 Encircled Plaques Of Exudates 3 Hard Exudatesmentioning

confidence: 99%

IM-EDRD from Retinal Fundus Images Using Multi-Level Classification Techniques

Karthikeyan¹,

Anita²

2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

show abstract

“…Threshold‐based methods exploit the variation in color intensity between different image regions. These methods depend on the global image gray‐level or the local image gray‐level (Phillips et al, 1993; Wisaeng et al, 2015). Pereira et al (2015) combined the threshold method with the optimizer of the ant colony to segment exudates.…”

Section: Literature Reviewmentioning

confidence: 99%

Semantic segmentation of retinal exudates using a residual encoder–decoder architecture in diabetic retinopathy

Manan,

Jinchao,

Khan

et al. 2023

Microscopy Res & Technique

View full text Add to dashboard Cite

Exudates are a common sign of diabetic retinopathy, which is a disease that affects the blood vessels in the retina. Early detection of exudates is critical to avoiding vision problems through continuous screening and treatment. In traditional clinical practice, the involved lesions are manually detected using photographs of the fundus. However, this task is cumbersome and time‐consuming and requires intense effort due to the small size of the lesion and the low contrast of the images. Thus, computer‐assisted diagnosis of retinal disease based on the detection of red lesions has been actively explored recently. In this paper, we present a comparison of deep convolutional neural network (CNN) architectures and propose a residual CNN with residual skip connections to reduce the parameter for the semantic segmentation of exudates in retinal images. A suitable image augmentation technique is used to improve the performance of network architecture. The proposed network can robustly segment exudates with high accuracy, which makes it suitable for diabetic retinopathy screening. A comparative performance analysis of three benchmark databases: E‐ophtha, DIARETDB1, and Hamilton Ophthalmology Institute's Macular Edema, is presented. The proposed method achieves a precision of 0.95, 0.92, 0.97, accuracy of 0.98, 0.98, 0.98, sensitivity of 0.97, 0.95, 0.95, specificity of 0.99, 0.99, 0.99, and area under the curve of 0.97, 0.94, and 0.96, respectively.Research Highlights The research focuses on the detection and segmentation of exudates in diabetic retinopathy, a disease affecting the retina. Early detection of exudates is important to avoid vision problems and requires continuous screening and treatment. Currently, manual detection is time‐consuming and requires intense effort. The authors compare qualitative results of the state‐of‐the‐art convolutional neural network (CNN) architectures and propose a computer‐assisted diagnosis approach based on deep learning, using a residual CNN with residual skip connections to reduce parameters. The proposed method is evaluated on three benchmark databases and demonstrates high accuracy and suitability for diabetic retinopathy screening.

show abstract

“…Threshold-based methods exploit the variation in color intensity between different image regions. These methods depends on the global image grey-level or the local image grey-level [32], [33]. Pereira et al [34] combined threshold method with the optimizer of the ant colony to segment exudates.…”

Section: Literature Reviewmentioning

confidence: 99%

A Residual Encoder-Decoder Network for Segmentation of Retinal Image-Based Exudates in Diabetic Retinopathy Screening

Manan¹,

Saadat²,

Arsalan³

et al. 2022

Preprint

View full text Add to dashboard Cite

Diabetic retinopathy refers to the pathology of the retina induced by diabetes and is one of the leading causes of preventable blindness in the world. Early detection of diabetic retinopathy is critical to avoid vision problem through continuous screening and treatment. In traditional clinical practice, the involved lesions are manually detected using photographs of the fundus. However, this task is cumbersome and time-consuming and requires intense effort due to the small size of lesion and low contrast of the images. Thus, computer-assisted diagnosis of diabetic retinopathy based on the detection of red lesions is actively being explored recently. In this paper, we present a convolutional neural network with residual skip connection for the segmentation of exudates in retinal images. To improve the performance of network architecture, a suitable image augmentation technique is used. The proposed network can robustly segment exudates with high accuracy, which makes it suitable for diabetic retinopathy screening. Comparative performance analysis of three benchmark databases: HEI-MED, E-ophtha, and DiaretDB1 is presented. It is shown that the proposed method achieves accuracy (0.98, 0.99, 0.98) and sensitivity (0.97, 0.92, and 0.95) on E-ophtha, HEI-MED, and DiaReTDB1, respectively.

show abstract

Automatic detection of exudates in retinal images based on threshold moving average models

Cited by 12 publications

References 8 publications

IM-EDRD from Retinal Fundus Images Using Multi-Level Classification Techniques

IM-EDRD from Retinal Fundus Images Using Multi-Level Classification Techniques

Semantic segmentation of retinal exudates using a residual encoder–decoder architecture in diabetic retinopathy

A Residual Encoder-Decoder Network for Segmentation of Retinal Image-Based Exudates in Diabetic Retinopathy Screening

Contact Info

Product

Resources

About