A location-to-segmentation strategy for automatic exudate segmentation in colour retinal fundus images

Liu, Qing; Zou, Beiji; Chen, Jie; Ke, Wei; Yue, Kejuan; Chen, Zailiang; Zhao, Guoying

doi:10.1016/j.compmedimag.2016.09.001

Cited by 91 publications

(41 citation statements)

References 19 publications

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“…Locating the exudate region patches and segmenting the exudate from that patch is exhibited by Qing Liu et al [41]. The whole procedure is divided into three phases, an anatomic structure evacuation, matched filter based fundamental vessels division technique and a saliency based optic disc division strategy.…”

Section: Detection Of Exudatesmentioning

confidence: 99%

Detection of Lesions for Diabetic Retinopathy By using Machine Learning Algorithms

Jebran P*,

Gupta

2020

IJRTE

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In this paper existing writing for computer added diagnosis (CAD) based identification of lesions that might be connected in the early finding of Diabetic Retinopathy (DR) is talked about. The recognition of sores, for example, Microaneurysms (MA), Hemorrhages (HEM) and Exudates (EX) are incorporated in this paper. A range of methodologies starting from conventional morphology to deep learning techniques have been discussed. The different strategies like hand crafted feature extraction to automated CNN based component extraction, single lesion identification to multi sore recognition have been explored. The different stages in each methods beginning from the image preprocessing to classification stage are investigated. The exhibition of the proposed strategies are outlined by various performance measurement parameters and their used data sets are tabulated. Toward the end we examined the future headings.

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Section: Detection Of Exudatesmentioning

confidence: 99%

Detection of Lesions for Diabetic Retinopathy By using Machine Learning Algorithms

Jebran P*,

Gupta

2020

IJRTE

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“…Zhao et al used the intensity and compactness features to generate a saliency map, and segment the precise LK area by using a graph‐cut model. Liu et al presented a location‐to‐segmentation strategy for automatic EX segmentation in color retinal fundus images.…”

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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“…For example, the cup-to-disk ratio is used for the assessment of the progress of glaucoma [2,3] . On the other hand, the location of the OD facilitates the detection of fovea [4] , exudate segmentation [5] , etc. Numerous works about the OD segmentation have been proposed in the literature.…”

Section: Introductionmentioning

confidence: 99%

Saliency‐Based Segmentation of Optic Disc in Retinal Images

Zou¹,

Liu²,

Yue

et al. 2019

Chin. j. electron.

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Accurate segmentation of optic disc (OD) is significant for the automation of retinal analysis and retinal diseases screening. This paper proposes a novel optic disc segmentation method based on the saliency. It includes two stages:optic disc location and saliency-based segmentation. In the location stage, the OD is detected using a matched template and the density of the vessels. In the segmentation stage, we treat the OD as the salient object and formulate it as a saliency detection problem. To measure the saliency of a region, the boundary prior and the connectivity prior are exploited. Then geodesic distance to the window boundary is computed to measure the cost the region spends to reach the window boundary. After a threshold and ellipse fitting, we obtain the OD. Experimental results on two public databases for OD segmentation show that the proposed method achieves thestate-of-the-art performance.

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A location-to-segmentation strategy for automatic exudate segmentation in colour retinal fundus images

Cited by 91 publications

References 19 publications

Detection of Lesions for Diabetic Retinopathy By using Machine Learning Algorithms

Detection of Lesions for Diabetic Retinopathy By using Machine Learning Algorithms

Automated retinal lesion detection via image saliency analysis

Saliency‐Based Segmentation of Optic Disc in Retinal Images

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