Adaptive Thresholding Technique for Retinal Vessel Segmentation Based on GLCM-Energy Information

Mapayi, Temitope; Viriri, Serestina; Tapamo, Jules-Raymond

doi:10.1155/2015/597475

Cited by 60 publications

(28 citation statements)

References 36 publications

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“…Fully connected conditional random field model efficiently extract the thin and elongated structure of retinal vessels structure [16]. Mapayi et al [17], presented Gray Level Cooccurrence Matrix (GLCM) based on adaptive thresholding to extract retinal vessel tree. Zhao et al [18], suggested a graph cut approach for segmentation of retinal vasculature.…”

Section: Review Of Segmentation Methodsmentioning

confidence: 99%

B-COSFIRE filter and VLM based retinal blood vessels segmentation and denoising

Khan

Khaliq

Shahid

2016

2016 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube)

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Diabetic retinopathy (DR) is the major ophthalmic disorder because of variation in veins structure which may cause blindness. The retinal vein morphology distinguishes the progressive phases of various sight debilitating maladies and consequently clears an approach to characterize its seriousness. The proposed method for retinal blood vessels detection consists of two major processes: denoising and vasculature segmentation. First, we used denoising preprocessing steps which comprises of Contrast Limited Adaptive Histogram Equalization (CLAHE) for contrast enhancement along with morphological filters to remove low frequency noise, followed by masking to excerpt Region Of Interest (ROI) and difference image of low pass filter to suppress high frequency noise. Adaptive thresholding has been used for vessels segmentation, followed by postprocessing to eliminate unconnected pixels and to obtain Vessel Location Map (VLM). Dilate operation has been used to enhance vessels diameter. In the second step, Combination Of Shifted Filter Responses (B-COSFIRE) for vasculature enhancement along with adaptive thresholding has been applied to segment vessel and background pixels. B represents the bar/vessel like structure. Finally, using pixel by pixel AND operation between VLM and the output of adaptive thresholding, to obtain desired binary image. The proposed framework has been validated on DRIVE and STARE images datasets and compared with other recent approaches for retinal blood vessels segmentation. The proposed scheme provides good results in the term of Accuracy (Acc), Sensitivity (Se) and Specificity (Sp) as compared to other competing methods.

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Section: Review Of Segmentation Methodsmentioning

confidence: 99%

B-COSFIRE filter and VLM based retinal blood vessels segmentation and denoising

Khan

Khaliq

Shahid

2016

2016 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube)

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“…In [21], Odstrcilik et al presented a method that extract the blood vessel using kittler minimum error thresholding method. In [22], Mapayi et al proposed a local adaptive thresholding method using gray level co-occurrence matrix-energy information to segment retinal blood vessels. In [23], Panda et al used a novel binary Hausdorff symmetry measure based seeded region growing technique to segment retinal blood vessels.…”

Section: Introductionmentioning

confidence: 99%

Retinal Blood Vessel Extraction from Fundus Images Using Enhancement Filtering and Clustering

Parida

Dash

Bhoi³

2020

ELCVIA

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Screening of vision troubling eye diseases by segmenting fundus images eases the danger of loss of sight of people. Computer assisted analysis can play an important role in the forthcoming health care system universally. Therefore, this paper presents a clustering based method for extraction of retinal vasculature from ophthalmoscope images. The method starts with image enhancement by contrast limited adaptive histogram equalization (CLAHE) from which feature extraction is accomplished using Gabor filter followed by enhancement of extracted features with Hessian based enhancement filters. It then extracts the vessels using Kmean clustering technique. Finally, the method ends with the application of a morphological cleaning operation to get the ultimate vessel segmented image. The performance of the proposed method is evaluated by taking two different publicly available Digital retinal images for vessel extraction (DRIVE) and Child heart and health study in England (CHASE_DB1) databases using nine different performance matrices. It gives average accuracies of 0.952 and 0.951 for DRIVE and CHASE_DB1 databases, respectively.

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“…Numerous approaches of adaptive thresholding segmentation based for retinal image analysis have been proposed for the detection of BV, OD, and red lesions [20–25]. BV, both large and thin, in retinal images are unable to segment accurately within a global threshold value . Therefore, local thresholding is required.…”

Section: Introductionmentioning

confidence: 99%

Cotton wool spots detection in diabetic retinopathy based on adaptive thresholding and ant colony optimization coupling support vector machine

Sreng

Maneerat

Hamamoto

et al. 2019

IEEJ Transactions Elec Engng

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Diabetic retinopathy is the major issue of diabetes-induced blindness worldwide but is curable if detected in time. Cotton wool spots (CWSs) are the critical lesions of diabetic retinopathy, which indicate not only advanced nonproliferative but also preproliferative diabetic retinopathy. It is crucial to detect CWSs for grading the severity of diabetic retinopathy. By grading the severity of diabetic retinopathy accurately, the eye specialist can make an effective treatment plan to protect the patient's vision against blindness. CWSs detection remains challenging because of their uneven appearance, in which some CWSs are not clearly visible and some resemble hard exudates. This paper proposed an automatic CWS detection method based on adaptive thresholding and ant colony optimization (ACO) coupled with support vector machine (SVM). One-hundred and sixtytwo features from five feature sets, namely morphologies, first-order statistics, gray-level co-occurrence matrix, gray-level run length matrix, and lacunarity, are extracted, and then four feature selection methods,namely genetic algorithm, particle swarm optimization, stepwise method, and ACO, are coupled with SVM classifiers. The evaluation results of the proposed methods on local, standard diabetic retinopathy database calibration level 1, and high-resolution fundus image database datasets containing 319 images indicate that ACO coupling cubic SVM performs better than the other pairs with sensitivity 90.16%, specificity 97.92%, accuracy 96.96%, and area under receiver operating characteristic curve 97.19%. , since 2009. He is currently a Dean of the School of Information and Telecommunication Engineering, Tokai University. His current research interests include information design. Especially, he is interested in medical information, human interface design, and virtual reality. He has about 60 papers published in Transactions and Journals and about 85 papers presented at international conferences. Prof. Hamamoto is a member of the IEEE and many national societies in Japan. He also takes active part in national societies as a committee member. RonakornPanjaphongse (Non-member) received the M.D. degree from the Faculty of Medicine Siriraj Hospital, Bangkok, Thailand, in 2001. He is currently the Head of the Retinal Unit, Ophthalmology Department, Bhumibol Adulyadej Hospital, Royal Thai Air Force, Bangkok, Thailand. His interests include studies related to retinal diseases, especially in diabetic retinopathy, including applying artificial intelligence for medical imaging.

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Adaptive Thresholding Technique for Retinal Vessel Segmentation Based on GLCM-Energy Information

Cited by 60 publications

References 36 publications

B-COSFIRE filter and VLM based retinal blood vessels segmentation and denoising

B-COSFIRE filter and VLM based retinal blood vessels segmentation and denoising

Retinal Blood Vessel Extraction from Fundus Images Using Enhancement Filtering and Clustering

Cotton wool spots detection in diabetic retinopathy based on adaptive thresholding and ant colony optimization coupling support vector machine

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