Exudate segmentation in fundus images using an ant colony optimization approach

Pereira, Carla; Gonçalves, Luís G.; Ferreira, Manuel

doi:10.1016/j.ins.2014.10.059

Cited by 76 publications

(30 citation statements)

References 30 publications

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“…This classification can model shapes of various lesions efficiently regardless of their variability in appearance, texture, or size. A multiagent system was proposed in which uses gradient patterns and Gaussian fitting parameters in different directions to segment MA. Dai et al employed gradient vector analysis and a class‐imbalance classifier to determine MA candidates.…”

Section: Related Workmentioning

confidence: 99%

Automated retinal lesion detection via image saliency analysis

et al. 2019

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Background and objective The detection of abnormalities such as lesions or leakage from retinal images is an important health informatics task for automated early diagnosis of diabetic and malarial retinopathy or other eye diseases, in order to prevent blindness and common systematic conditions. In this work, we propose a novel retinal lesion detection method by adapting the concepts of saliency. Methods Retinal images are first segmented as superpixels, two new saliency feature representations: uniqueness and compactness, are then derived to represent the superpixels. The pixel level saliency is then estimated from these superpixel saliency values via a bilateral filter. These extracted saliency features form a matrix for low‐rank analysis to achieve saliency detection. The precise contour of a lesion is finally extracted from the generated saliency map after removing confounding structures such as blood vessels, the optic disk, and the fovea. The main novelty of this method is that it is an effective tool for detecting different abnormalities at the pixel level from different modalities of retinal images, without the need to tune parameters. Results To evaluate its effectiveness, we have applied our method to seven public datasets of diabetic and malarial retinopathy with four different types of lesions: exudate, hemorrhage, microaneurysms, and leakage. The evaluation was undertaken at the pixel level, lesion level, or image level according to ground truth availability in these datasets. Conclusions The experimental results show that the proposed method outperforms existing state‐of‐the‐art ones in applicability, effectiveness, and accuracy.

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Section: Related Workmentioning

confidence: 99%

Automated retinal lesion detection via image saliency analysis

et al. 2019

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“…Threshold based methods exploit differences in colour intensity between various image regions. Pereira et al 8 combined a thresholding approach with the ant colony optimizer to segment EXs. EX candidates were identified using a thresholding method, whereas the unsupervised ant colony optimizer was used to enhance EXs edges.…”

Section: Related Workmentioning

confidence: 99%

Exudate segmentation using fully convolutional neural networks and inception modules

Chudzik

Majumdar

Calivá

et al. 2018

Medical Imaging 2018: Image Processing

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Diabetic retinopathy is an eye disease associated with diabetes mellitus and also it is the leading cause of preventable blindness in working-age population. Early detection and treatment of DR is essential to prevent vision loss. Exudates are one of the earliest signs of diabetic retinopathy. This paper proposes an automatic method for the detection and segmentation of exudates in fundus photographies. A novel fully convolutional neural network architecture with Inception modules is proposed. Compared to other methods it does not require the removal of other anatomical structures. Furthermore, a transfer learning approach is applied between small datasets of different modalities from the same domain. To the best of authors' knowledge, it is the first time that such approach has been used in the exudate segmentation domain. The proposed method was evaluated using publicly available E-Ophtha datasets. It achieved better results than the state-of-the-art methods in terms of sensitivity and specificity metrics. The proposed algorithm accomplished better results using a diseased/not diseased evaluation scenario which indicates its applicability for screening purposes. Simplicity, performance, efficiency and robustness of the proposed method demonstrate its suitability for diabetic retinopathy screening applications.

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“…For increasing the possibility of retinopathy screening, many techniques are designed for automated lesion detection (Osareh, Mirmehdi, Thomas, & Markham, ). Ant colony optimization method is used for the detection of exudates (Pereira, Gonçalves, & Ferreira, ). Local variation operator, split/merge, and adaptive threshold methods are used for the diagnosis of HE.…”

Section: Related Workmentioning

confidence: 99%

Diabetic retinopathy detection and classification using hybrid feature set

Amin

Sharif

Rehman

et al. 2018

Microscopy Res & Technique

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Complicated stages of diabetes are the major cause of Diabetic Retinopathy (DR) and no symptoms appear at the initial stage of DR. At the early stage diagnosis of DR, screening and treatment may reduce vision harm. In this work, an automated technique is applied for detection and classification of DR. A local contrast enhancement method is used on grayscale images to enhance the region of interest. An adaptive threshold method with mathematical morphology is used for the accurate lesions region segmentation. After that, the geometrical and statistical features are fused for better classification. The proposed method is validated on DIARETDB1, E‐ophtha, Messidor, and local data sets with different metrics such as area under the curve (AUC) and accuracy (ACC).

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Exudate segmentation in fundus images using an ant colony optimization approach

Cited by 76 publications

References 30 publications

Automated retinal lesion detection via image saliency analysis

Automated retinal lesion detection via image saliency analysis

Exudate segmentation using fully convolutional neural networks and inception modules

Diabetic retinopathy detection and classification using hybrid feature set

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