Optic Disc Segmentation in Retinal Fundus Images Using Morphological Techniques and Intensity Thresholding

Samawi, Hayder Jaber; Al-Sultan, Ali Yakoob; Al-Saadi, Enas Hamood

doi:10.1109/csase48920.2020.9142061

Cited by 7 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The technique of delineating and subsequently removing the OD from the images is known as segmentation of the OD. High-intermediate gray-scale pixels can be used to place the OD since it is the portion of the retina that is most lit [4]. This method is not the most effective, despite the fact that numerous high-intensity areas, such as exudates, might be seen in the retinal images and cause the OD to appear in the wrong place.…”

Section: Introductionmentioning

confidence: 99%

The Optic Disc Detection and Segmentation in Retinal Fundus Images Utilizing You Only Look Once (YOLO) Method

Jabbar Hussein,

Hamood Al-Saadi

2024

IJCDS

View full text Add to dashboard Cite

Automated analysis of retinal images is a crucial diagnostic method for the early detection of disorders affecting the eyes, such as glaucoma, diabetic macula edema (DME), and retinopathy brought on by diabetes. The current study introduces a reliable technique for segmenting and detecting optic discs using a deep learning-based technique. This is comparable to the initial stage of creating a diagnostic system supported by a computer for diabetic macula edema in retinal images. The suggested approach is predicated on the recommended approach using the YOLO (You Only Look Once) algorithm for detection objects and segmentation for bounding boxes that belong to the same category, comparing the Intersection over Union (IOU) values of each bounding box with those of the others. If IOU values are higher than thresholds, they will consider them the same targets and maintain the boundary boxes with the highest reliability. Three retinal image databases that are accessible to the public are used to quantitatively assess the technique: Messidor-1, Messidor-2, and the IDRID Database. The technique yields a success rate of 99.5% for optic disc identification, a precision of 99.9%, and a recall of 100% for Messidor-1, and testing for all Messidor-2 and IDRID databases accepts accuracy of 99.1% and 98.7% respectively. When it comes to the identification and border extraction of the optic disc, this special technique has demonstrated a notable improvement over the previous approach.

show abstract

Section: Introductionmentioning

confidence: 99%

The Optic Disc Detection and Segmentation in Retinal Fundus Images Utilizing You Only Look Once (YOLO) Method

Jabbar Hussein,

Hamood Al-Saadi

2024

IJCDS

View full text Add to dashboard Cite

show abstract

“…Zhang et al 7 proposed particle swarm optimization (PSO) enhanced ensemble deep neural network for optic disc segmentation of retinal images, and used an improved particle swarm algorithm with six search mechanisms, which can make the search process more diversified. Samawi et al 11 performed optic disc segmentation on retinal fundus images based on morphological techniques and intensity thresholding. Firstly, the retinal fundus image was preprocessed, then the optic disc was detected by intensity threshold and morphology, and finally the color channel of optic disc was extracted from the original image, which achieved good results.…”

Section: Introductionmentioning

confidence: 99%

Optic disc segmentation in retinal fundus images using improved CE-Net

wang

Huang²

2022

Fourteenth International Conference on Digital Image Processing (ICDIP 2022)

View full text Add to dashboard Cite

Diabetic retinopathy is one of the main complications of diabetes and the most important factor leading to blindness in the late stage of the disease. It often manifests as one or more lesions in clinical diagnosis. In order to reduce the difficulty of detection, it is of great significance to segment the optic disc in retinal images. This paper proposes an improved context encoding network architecture (CE-Net) for segmentation of the optic disc portion in diabetic retinal images. The network architecture is divided into three parts: feature encoder module, context extractor module and feature decoder module. The context extractor module consists of an improved dense atrous convolutional block (DAC) and residual multi-kernel pooling (RMP). Experimental result shows that the optimal network model generated by the improved CE-Net architecture has good performance on the Indian Diabetic Retinopathy Image Dataset (IDRID), and compared with other methods, our method has the lowest mean overlap error and the highest accuracy and sensitivity.

show abstract

Automated segmentation and quantitative analysis of optic disc and fovea in fundus images

Kim

Lee

Kim

2021

Multimed Tools Appl

View full text Add to dashboard Cite

Fundus image is widely used diagnosis method and involves the retinal tissues which can be important biomarkers for diagnosing diseases. Many studies have proposed automatic algorithms to detect the optic disc (OD) and fovea. However, they showed some limitations. Although the precise regions of retinal tissues are clinically important, most of these studies focused on the localization not the segmentation. Also, they did not sufficiently prove the clinical effectiveness of the methods using quantitative analysis. Furthermore, many of them have researched about the single retinal tissue. To compensate for these limitations, this study proposed automated segmentation method for both of the OD and fovea. In this study, the dataset was acquired from the DRIVE and Drions databases, and additional ground truth dataset was obtained from an ophthalmologist. The original fundus image was preprocessed to remove noise and enhance contrast. And the retinal vessel was segmented to use for the OD and fovea segmentation. In the OD and fovea segmentation step, a region of interest was designated based on the image features to increase the segmentation accuracy. To segment the OD, the retinal vessel was removed and substituted based on the intensity value of the four nearest non-vessel pixels. Finally, the OD and fovea regions were segmented based on the image features including intensity, shape and size. The proposed method was evaluated by quantitative analysis using eight methods. As a result, the proposed method showed high segmentation performance for the OD and fovea with accuracy of 99.18 and 99.80 % on the DRIVE database.

show abstract

Optic Disc Segmentation in Retinal Fundus Images Using Morphological Techniques and Intensity Thresholding

Cited by 7 publications

References 17 publications

The Optic Disc Detection and Segmentation in Retinal Fundus Images Utilizing You Only Look Once (YOLO) Method

The Optic Disc Detection and Segmentation in Retinal Fundus Images Utilizing You Only Look Once (YOLO) Method

Optic disc segmentation in retinal fundus images using improved CE-Net

Automated segmentation and quantitative analysis of optic disc and fovea in fundus images

Contact Info

Product

Resources

About