A region growing and local adaptive thresholding-based optic disc detection

Khan, Tariq M.; Mehmood, Mehwish; Naqvi, Syed S.; Butt, Muhammad Fasih Uddin

doi:10.1371/journal.pone.0227566

Cited by 26 publications

(9 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We compared the factorized gradient vector flow (FGVF) 8 , 9 used in our work against four other comparative methods: alternated deflation-inflation gradient vector flow (ADI-GVF) 37 , traditional gradient vector flow (GVF) 36 , region growing (RG) 29 , and super-pixel clustering (SPC) 30 . All methods except super-pixel clustering required initial points.…”

Section: Numerical Results and Discussionmentioning

confidence: 99%

“…The intensity, edge, and area features are considered in the method, and the Edge indicator function (EIF) is computed to differentiate OD and OC edges 1341 images from Dhristi-GS, DRIVE, REFUGE Jaccard 93.20, Dice 96.48, Accuracy 99.77 Dashtbozorg et al 27 Using two sliding band filters (SBF): low-resolution SBF for initial OD center location estimation and high-resolution producing band support points for initial OD boundary 1339 images from ONHSD, MESSIDOR, INSPIRE-AVR Overlap 89, Dice 93.73, Accuracy 99.87 Zaaboub et al 28 Using the saliency mask on the fundus images to localization region. Irregular shape OD boundary is refined by ellipse fitting 2050 images from RimOne, IDRID, Chase, Drive, HRF, Drishti, DRIONS, Bin Rushed, Magrabia, MESSIDOR, LocalDB Accuracy 99.7, Dice 92.86, Jaccard 88.95, Sensitivity 97.98, Specificity 99.77 Khan et al 29 Using the region growing and adaptive thresholding methods. Eccentricity and size are used for the final OD selection 2054 images from DRIONS, MESSIDOR, ONHSD, DIARETDB1, DRISHTI, RIM-ONE Sensitivity 96.49, Specificity 99.75, Accuracy 99.60, Wilson and Mahesh 30 Using superpixels with the k-mean algorithm 1310 images from DRIONS, MESSIDOR Jaccard 84.23, Dice 90.84, Accuracy 99.34 Rehman et al 31 Using a simple linear iterative clustering algorithm technique combined with the features-based classification.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automated optic disk segmentation for optic disk edema classification using factorized gradient vector flow

Naing,

Aimmanee

2024

Sci Rep

View full text Add to dashboard Cite

One significant ocular symptom of neuro-ophthalmic disorders of the optic disk (OD) is optic disk edema (ODE). The etiologies of ODE are broad, with various symptoms and effects. Early detection of ODE can prevent potential vision loss and fatal vision problems. The texture of edematous OD significantly differs from the non-edematous OD in retinal images. As a result, techniques that usually work for non-edematous cases may not work well for edematous cases. We propose a fully automatic OD classification of edematous and non-edematous OD on fundus image collections containing a mixture of edematous and non-edematous ODs. The proposed algorithm involved localization, segmentation, and classification of edematous and non-edematous OD. The factorized gradient vector flow (FGVF) was used to segment the ODs. The OD type was classified using a linear support vector machine (SVM) based on 27 features extracted from the vessels, GLCM, color, and intensity line profile. The proposed method was tested on 295 images with 146 edematous cases and 149 non-edematous cases from three datasets. The segmentation achieves an average precision of 88.41%, recall of 89.35%, and F1-Score of 86.53%. The average classification accuracy is 99.40% and outperforms the state-of-the-art method by 3.43%.

show abstract

Section: Numerical Results and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated optic disk segmentation for optic disk edema classification using factorized gradient vector flow

Naing,

Aimmanee

2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The circular area at the rear of the eye’s interior where the optic nerve links to the retina also called as the optic nerve head (ONH). The “cup” in the middle of the optic disc is usually very thin in contrast to the entire optic disc [ 91 ]. To separate the related sections of the retinal image and measure the cup-to-disk ratio, segmentation techniques such as optic disc and optic cup segmentation are used.…”

Section: Dr Screening Methodsmentioning

confidence: 99%

Recent developments on computer aided systems for diagnosis of diabetic retinopathy: a review

Dubey

Dixit

2022

Multimed Tools Appl

View full text Add to dashboard Cite

Diabetes is a long-term condition in which the pancreas quits producing insulin or the body’s insulin isn’t utilised properly. One of the signs of diabetes is Diabetic Retinopathy. Diabetic retinopathy is the most prevalent type of diabetes, if remains unaddressed, diabetic retinopathy can affect all diabetics and become very serious, raising the chances of blindness. It is a chronic systemic condition that affects up to 80% of patients for more than ten years. Many researchers believe that if diabetes individuals are diagnosed early enough, they can be rescued from the condition in 90% of cases. Diabetes damages the capillaries, which are microscopic blood vessels in the retina. On images, blood vessel damage is usually noticeable. Therefore, in this study, several traditional, as well as deep learning-based approaches, are reviewed for the classification and detection of this particular diabetic-based eye disease known as diabetic retinopathy, and also the advantage of one approach over the other is also described. Along with the approaches, the dataset and the evaluation metrics useful for DR detection and classification are also discussed. The main finding of this study is to aware researchers about the different challenges occurs while detecting diabetic retinopathy using computer vision, deep learning techniques. Therefore, a purpose of this review paper is to sum up all the major aspects while detecting DR like lesion identification, classification and segmentation, security attacks on the deep learning models, proper categorization of datasets and evaluation metrics. As deep learning models are quite expensive and more prone to security attacks thus, in future it is advisable to develop a refined, reliable and robust model which overcomes all these aspects which are commonly found while designing deep learning models.

show abstract

“…Khan et al [25] performed local adaptive thresholding and a region growing method to detect the boundary of OD. The method creates an initial seed by searching for the center of the localized OD.…”

Section: Od Segmentationmentioning

confidence: 99%

Glaucoma Detection in Mobile Phone Retinal Images Based on ADI-GVF Segmentation with EM initialization

et al. 2021

View full text Add to dashboard Cite

The advanced development of mobile phone and lens technology has made retinal imaging more convenient than ever before. In the digital health era, mobile phone fundus photography has evolved into a low-cost alternative to the standard ophthalmoscope. Existing image processing algorithms have a problem with handling the narrow field of view and poor quality of retinal images from a mobile phone. This paper enhances the accuracy of our previously proposed scheme, ADI-GVF snakes, to improve the segmentation of the optic disk (OD) and the optic cup (OC) for glaucoma pre-screening [1] from retinal images obtained from a mobile phone. This work integrated a better OD localization method, namely, the exclusion method (EM) with ADI-GVF segmentation for the OD and the OC. The improved algorithm can segment the regions of the OD and OC more accurately, resulting in a more precise value of the cup-to-disk area ratio (CDAR). The proposed method yields as high as 93.33% for true positive rate (TPR) and 93.87% for true negative rate (TNR) and as low as 6.12% and 6.66% for false omission rate (FOR), and false discovery rate (FDR). It also improves TPR, TNR, FOR, and FDR of the previous scheme [1] by 4.45%, 4.08%, 4.08%, and 4.44% respectively.

show abstract

A region growing and local adaptive thresholding-based optic disc detection

Cited by 26 publications

References 30 publications

Automated optic disk segmentation for optic disk edema classification using factorized gradient vector flow

Automated optic disk segmentation for optic disk edema classification using factorized gradient vector flow

Recent developments on computer aided systems for diagnosis of diabetic retinopathy: a review

Glaucoma Detection in Mobile Phone Retinal Images Based on ADI-GVF Segmentation with EM initialization

Contact Info

Product

Resources

About