scholarly journals Automated Method for Optic Disc Detection and Elimination in Digital Fundus Images

2019 ◽  
Vol 8 (4) ◽  
pp. 12558-12563
Keyword(s):  
Diabetic Retinopathy ◽  
Optic Disc ◽  
Retinal Diseases ◽  
Image Analysis System ◽  
Fundus Images ◽  
Connected Component ◽  
Automated Method ◽  
Novel Method ◽  
Disc Region ◽  
Analysis System

Localizing, segmenting and eliminating the optic disc region of a fundus image is a prerequisite task in the automatic investigation of a number of retinal diseases such as Diabetic retinopathy, Glaucoma, Macular Edema, etc. Accurate detection of optic disc is a challenging task due to a number of reasons. Optic disc in most fundus images does not exhibit clear disc boundaries and there are number of blood vessels crossing it. An important task in automated retinal image analysis system is the detection and elimination of optic disc because the lesion regions in diabetic retinopathy closely resemble the color and texture of an optic disc. Hence, eliminating the optic disc region can improve the performance of diabetic retinopathy detection. The proposed work presents a novel method for optic disc segmentation which is not restricted by the location of the optic disc on the retina. The proposed algorithm localizes the position of the optic disc that is independent of its location and dynamically finds its center. The proposed method is tested on images from DRISHTI-GS, DIARETDB1, DRIONS-DB and DRIVE databases based on morphological operation and finding the largest connected component. The precision values of segmentation for digital fundus images from DRISHTI-GS, DIARETDB1, DRIONS-DB, and DRIVE databases are 0.98, 0.99, 0.98 and 0.99 respectively using the proposed method. The algorithm has yielded consistent high values of precision and recall indicating its robustness and efficiency.

scholarly journals Automated feature-based grading and progression analysis of diabetic retinopathy

Eye ◽  
2021 ◽  
Author(s):  
Lutfiah Al-Turk ◽  
James Wawrzynski ◽  
Su Wang ◽  
Paul Krause ◽  
George M. Saleh ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Diabetic Macular Oedema ◽  
Large Datasets ◽  
Image Analysis System ◽  
Learning Approaches ◽  
External Evaluation ◽  
Severity Scale ◽  
Grading Systems ◽  
Feature Based ◽  
Analysis System

Abstract Background In diabetic retinopathy (DR) screening programmes feature-based grading guidelines are used by human graders. However, recent deep learning approaches have focused on end to end learning, based on labelled data at the whole image level. Most predictions from such software offer a direct grading output without information about the retinal features responsible for the grade. In this work, we demonstrate a feature based retinal image analysis system, which aims to support flexible grading and monitor progression. Methods The system was evaluated against images that had been graded according to two different grading systems; The International Clinical Diabetic Retinopathy and Diabetic Macular Oedema Severity Scale and the UK’s National Screening Committee guidelines. Results External evaluation on large datasets collected from three nations (Kenya, Saudi Arabia and China) was carried out. On a DR referable level, sensitivity did not vary significantly between different DR grading schemes (91.2–94.2.0%) and there were excellent specificity values above 93% in all image sets. More importantly, no cases of severe non-proliferative DR, proliferative DR or DMO were missed. Conclusions We demonstrate the potential of an AI feature-based DR grading system that is not constrained to any specific grading scheme.

scholarly journals Region Focus Network for Joint Optic Disc and Cup Segmentation

2020 ◽  
Vol 34 (01) ◽  
pp. 751-758
Author(s):  
Ge Li ◽  
Changsheng Li ◽  
Chan Zeng ◽  
Peng Gao ◽  
Guotong Xie
Keyword(s):  
Optic Disc ◽  
Feature Representation ◽  
Fundus Images ◽  
Feature Pooling ◽  
The World ◽  
Optic Cup ◽  
Disc Region ◽  
Class Segmentation ◽  
Fusion Feature ◽  
Screening And Diagnosis

Glaucoma is one of the three leading causes of blindness in the world and is predicted to affect around 80 million people by 2020. The optic cup (OC) to optic disc (OD) ratio (CDR) in fundus images plays a pivotal role in the screening and diagnosis of glaucoma. Existing methods usually crop the optic disc region first, and subsequently perform segmentation in this region. However, these approaches come up with high complexities due to the separate operations. To remedy this issue, we propose a Region Focus Network (RF-Net) that innovatively integrates detection and multi-class segmentation into a unified architecture for end-to-end joint optic disc and cup segmentation with global optimization. The key idea of our method is designing a novel multi-class mask branch which generates a high-quality segmentation in the detected region for both disc and cup. To bridge the connection between the backbone and multi-class mask branch, a Fusion Feature Pooling (FFP) structure is presented to extract features from each level of the pyramid network and fuse them into a final feature representation for segmentation. Extensive experimental results on the REFUGE-2018 challenge dataset and the Drishti-GS dataset show that the proposed method achieves the best performance, compared with competitive approaches reported in the literature and the official leaderboard. Our code will be released soon.

scholarly journals Automated detection of optic disc in retinal fundus images using gabor filter kernels

2015 ◽  
Vol 5 (1) ◽  
pp. 36
Author(s):  
Baha Sen ◽  
Kemal Akyol ◽  
Safak Bayir ◽  
Hilal Kaya
Keyword(s):  
Diabetic Retinopathy ◽  
Optic Disc ◽  
Retinal Image ◽  
Gabor Filter ◽  
Blue Color ◽  
Fundus Images ◽  
Automatic Determination ◽  
Retinal Fundus Images ◽  
Retinal Fundus

<p>Identifying the position of the optic disc on the retinal fundus image is a technique that is often used in medical diagnosis, treatment and monitoring processes. Determination of the intensity of the bright colors that belongs to the optic disc on a normal retinal image by the help of image processing algorithms is a fairly easy process. However, determining the optic disc on a retinal image including the diabetic retinopathy disease is a more difficult process. The reason for this difficulty is the existence of many regions that have the same light intensity in different parts of the retina. In this study, a new method for supplying the automatic determination of the optic disc in a recursive manner is proposed. By the help of OpenCV library, automatic determination process of the optic disc on the retinal fundus images including the diabetic retinopathy disease, has been implemented. Circular regions with maximum brightness values in the retinal images that were normalized and passed through the denoising process were determined and these regions were analyzed if they are optic disc or not. This process basically consists of two steps: In the first step, the possible optic disc candidate regions were determined recursively and in the second step, by the help of Gabor filter kernels, these regions were analyzed and it’s provided to decide if they are optic disc or not. This study is based on a dataset that has 89 images including diabetic retinopathy disease. Performance of this system is tested on these images and also on the images that the red, green, blue color channels and Contrast Limited Adaptive Histogram Equalization (CLAHE) retinas were obtained. Most accurate determination of the position of the optic disc is obtained with retinas, implemented process CLAHE, including the best success rate of 89.88%.</p><p> </p>Keywords: Optic disc, diabetic retinopathy, recursively, circular region, gabor filter kernels.

Mathematical morphology for microaneurysm detection in fundus images

2019 ◽  
Vol 30 (5) ◽  
pp. 1135-1142 ◽  
Author(s):  
Shilpa Joshi ◽  
PT Karule
Keyword(s):  
Diabetic Retinopathy ◽  
Detection System ◽  
Automated Detection ◽  
Morphological Processing ◽  
Candidate Selection ◽  
Retinal Diseases ◽  
Fundus Images ◽  
Time Saving ◽  
Increased Risk ◽  
Sight Loss

Aim: Fundus image analysis is the basis for the better understanding of retinal diseases which are found due to diabetes. Detection of earlier markers such as microaneurysms that appear in fundus images combined with treatment proves beneficial to prevent further complications of diabetic retinopathy with an increased risk of sight loss. Methods: The proposed algorithm consists of three modules: (1) image enhancement through morphological processing; (2) the extraction and removal of red structures, such as blood vessels preceded by detection and removal of bright artefacts; (3) finally, the true microaneurysm candidate selection among other structures based on feature extraction set. Results: The proposed strategy is successfully evaluated on two publicly available databases containing both normal and pathological images. The sensitivity of 89.22%, specificity of 91% and accuracy of 92% achieved for the detection of microaneurysms for Diaretdb1 database images. The algorithm evaluation for microaneurysm detection has a sensitivity of 83% and specificity 82% for e-ophtha database. Conclusion: In automated detection system, the successful detection of the number of microaneurysms correlates with the stages of the retinal diseases and its early diagnosis. The results for true microaneurysm detection indicates it as a useful tool for screening colour fundus images, which proves time saving for counting of microaneurysms to follow Diabetic Retinopathy Grading Criteria.

scholarly journals Optic Disc Segmentation in Diabetic Retinopathy using Image Processing

2020 ◽  
pp. 236-240
Author(s):  
Prashant Vishwakarma ◽  
Somen Jaiswal ◽  
Jay Chandarana ◽  
Abhishek Vyas
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Optic Disc ◽  
Optic Disk ◽  
Diagnostic Tools ◽  
Fundus Images ◽  
Macula Edema ◽  
Crucial Feature ◽  
Image Processing Algorithms ◽  
Processing Algorithms

Diabetic Retinopathy and Glaucoma are optic diseases that involve optic disk identification, which is a crucial phase in the current diagnostic tools that can be computerized. When these diseases are identified early by any screening applications, measures may be taken to avoid blindness. Early indicators of the numerous illness such as Macula Edema, Diabetic Retinopathy and Glaucoma are the changes in the anatomy structures in the retina of the human eye which also has the inclusion of the retinal vasculature. Of these, the Optic Disc is the most crucial feature, as its visible factors are essential for the identification of glaucoma and other disease-related assessments called Diabetic Retinopathy. In this paper, we present methods to detect the likelihood of Diabetic Retinopathy being present from fundus images. This technique starts with pre-processing on the optic retinal image to concentrate on the main area of the disease that we need to identify. Afterwards we apply Image processing algorithms to detect the optic disk. Detecting the optic disc is vital because it is the origin of all the nerves and detecting the position and radius of optic disc can be used as the reference for approximating fovea i.e. a pit like area responsible for vision. Size and shape of optic disc is responsible for diagnosing the disease. Therefore, this paper addresses the analysis of different techniques to detect the optic disc.

scholarly journals Automatic Localization and Extraction Of Optic Disc Using Darwinian PSO and Morphological Operations

2019 ◽  
Vol 8 (3) ◽  
pp. 8209-8214
Keyword(s):  
Diabetic Retinopathy ◽  
Optic Disc ◽  
Automated System ◽  
Automatic Identification ◽  
Intensity Level ◽  
Morphological Operations ◽  
Retinal Diseases ◽  
Fundus Image ◽  
Scatter Plot ◽  
High Positive Correlation

Locating and earmarking of the optic disc (OD) is a crucial step in the automatic identification of retinal diseases. In advanced stage of proliferative diabetic retinopathy on disk , the delicate blood vessels starts to grow in the disk and hence needs to be clearly identified for better grading of diabetic retinopathy . Furthermore exudates and optic disc share almost same intensity level and may lead to wrong classification if the latter is not identified and removed before the classification. In this paper, we propose an efficient automated system for OD detection and extraction so that exudates are extracted more effectively which will improve overall accuracy in diagnosis of Diabetic Retinopathy. A novel multilevel thresholding optimized by Darwinian Particle swarm Optimization is adopted to detect optic disc in the fundus image. Later Morphological operations are performed to extract the optic disc with precision. The suggested algorithm is tested on four publically available databases like MESSIDOR, DRIVE, and DIARETDB1. Performance of the algorithm is analyzed from the scatter plot. From the scatter plot, it is observed that manually labeled and automatically detected OD centers have a high positive correlation.

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