Automatic Detection of Optic Disc in Digital Retinal Images

Wisaeng, Kittipol; Hiransakolwong, Nualsawat; Pothiruk, Ekkarat

doi:10.5120/15569-4105

Cited by 10 publications

(6 citation statements)

References 16 publications

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“…Wisaeng et al developed a method to detect the OD in low quality retinal images that have non uniform illumination and poor contrast [27]. Their methodology was based on a combination of mathematical morphology operations and Otsu's algorithms.…”

Section: Optic Disc (Od) Detection and Filteringmentioning

confidence: 99%

Automatic detection and classification of diabetic retinopathy from retinal fundus images

Biran¹

2021

Preprint

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Automatic Detection and Classification of Diabetic Retinopathy from Retinal Fundus Images by Abdullah Biran, Master of Applied Science, lectrical and computer engineering Department, Ryerson University, 2017. Diabetic Retinopathy (DR) is an eye disease that leads to blindness when it progresses to proliferative level. The earliest signs of DR are the appearance of red and yellow lesions on the retina called hemorrhages and exudates. Early diagnosis of DR prevents from blindness. In this thesis, an automatic algorithm for detecting diabetic retinopathy is presented. The algorithm is based on combination of several image processing techniques including Circular Hough Transform (CHT), Contrast Limited Adaptive Histogram Equalization (CLAHE), Gabor filter and thresholding. In addition, Support Vector Machine (SVM) classifier is used to classify retinal images into normal or abnormal cases of DR including non-proliferative (NPDR) or proliferative diabetic retinopathy (PDR). The proposed method has been tested on fundus images from Standard Diabetic Retinopathy Database (DIARETDB). The implementation of the presented methodology was done in MATLAB. The methodology is tested for sensitivity and accuracy.

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Section: Optic Disc (Od) Detection and Filteringmentioning

confidence: 99%

Automatic detection and classification of diabetic retinopathy from retinal fundus images

Biran¹

2021

Preprint

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“…The STARE database [4] consists of 20 images, in which 10 images have pathologies such as exudates and hemorrhages. The size of the images is 605 × 700 pixels.…”

Section: B Stare Datasetmentioning

confidence: 99%

Optic Disc Detection in Fundus Eye Images – A Detailed Survey

Prakash¹,

B²

2021

Proceedings of the First International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8 2021, Chennai, In

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Detection of Optic Disc is an important task in the automatic retinal disease screening system. From the literature, it is identified that diabetic retinopathy is the main reason for blindness among working people especially aged people. Identifying and curing diabetic retinopathy at the earlier stages may prevent the cause of blindness among people. In this paper, a detailed survey involving the various techniques in optic disc detection like Morphological Operation Technique, Hough transform Technique, Evolutionary computing technique, and Machine learning technique are made from various literature works. This paper also discusses the various publicly available dataset that could be useful in assessing the performance of the various techniques. It also discusses the result obtained from the various methods proposed by different scholars in this domain and emphasize the enhancement obtained by them.

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“…By using this method, optic disc can be carefully and clearly be extracted out. Optic disc is the most important characteristic in diagnosing glaucoma [17]. This owe to the fact that glaucoma is very hard to be roughly detected with only our eyes.…”

Section: Xθb = X-b = {Z∶(b+z)⊆x} (2)mentioning

confidence: 99%

Glaucoma detection of retinal images based on boundary segmentation

Zainudin

Nazari

Mustafa

et al. 2020

IJEECS

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<p>The rapid growth of technology makes it possible to implement in immediate diagnosis for patients using image processing. By using morphological processing and adaptive thresholding method for segmentation of optic disc and optic cup, various sizes of retinal fundus images captured through fundus camera from online databases can be processed. This paper explains the use of color channel separation method for pre-processing to remove noise for better optic disc and optic cup segmentation. Noise removal will improve image quality and in return help to increase segmentation standard. Then, morphological processing and adaptive thresholding method is used to extract out optic disc and optic cup from fundus image. The proposed method is tested on two publicly available online databases: RIM-ONE and DRIONS-DB. On RIM-ONE database, the average PSNR value acquired is 0.01891 and MSE is 65.62625. Meanwhile, for DRIONS-DB database, the best PSNR is 64.0928 and the MSE is 0.02647. In conclusion, the proposed method can successfully filter out any unwanted noise in the image and are able to help clearer optic disc and optic cup segmentation to be performed.</p>

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Automatic Detection of Optic Disc in Digital Retinal Images

Cited by 10 publications

References 16 publications

Automatic detection and classification of diabetic retinopathy from retinal fundus images

Automatic detection and classification of diabetic retinopathy from retinal fundus images

Optic Disc Detection in Fundus Eye Images – A Detailed Survey

Glaucoma detection of retinal images based on boundary segmentation

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