Comparative Study of Retinal Vessel Segmentation Based on Global Thresholding Techniques

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

doi:10.1155/2015/895267

Cited by 31 publications

(17 citation statements)

References 35 publications

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“…The significant difference in average sensitivity rate indicates the significant difference in the detection rate of the vessels and the significant difference in accuracy rate indicates the significant difference in the accuracy rate of the retinal vessel segmentation. Both measures are very important as good vessel segmentation method for efficient vessel analysis in ophthalmology requires that sensitivity and accuracy rates of the segmentation be good 2015). The statistical significance of a difference between two proportions is evaluated as…”

Section: Resultsmentioning

confidence: 99%

“…This section presents an unsupervised vessel segmentation approach to address the problems of inability to detect the thinner vessels (Martínez-Pérez et al;Vlachos and Dermatas;Saffarzadeh et al;2015) connectivity loss in vessel network (Jiang and Mojon;. Since unsupervised vessel segmentation methods are not dependent on labelled training set, the method investigated in this paper also overcomes the major drawback of supervised segmentation methods which is their high dependence on the labelled training with the retraining of the classifiers Marín et al;.…”

Section: Methodsmentioning

confidence: 99%

“…Cornforth et al (2005) applied wavelet analysis, supervised classifier probabilities and adaptive threshold procedures, as well as morphology-based techniques. A comparative study of global thresholding techniques previously implemented by Mapayi et al (2015) showed that global thresholding approaches are limited at efficiently segmenting thin retinal vessels. In another study, Mapayi et al (2014) implemented an adaptive thresholding technique utilizing different types of local homogeneity information for retinal vessel segmentation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic Retinal Vessel Detection and Tortuosity Measurement

Mapayi¹,

Tapamo²,

Viriri³

et al. 2016

Image Anal Stereol

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As retinopathies continue to be major causes of visual loss and blindness worldwide, early detection and management of these diseases will help achieve significant reduction of blindness cases. However, an efficient automatic retinal vessel segmentation approach remains a challenge. Since efficient vessel network detection is a very important step needed in ophthalmology for reliable retinal vessel characterization, this paper presents the study on the combination of difference image and k-means clustering for the segmentation of retinal vessels. Stationary points in the vessel center-lines are used to model the detection of twists in the vessel segments. The combination of arc-chord ratio with stationary points is used to compute tortuosity index. Experimental results show that the proposed k-means combined with difference image achieved a robust segmentation of retinal vessels. A maximum average accuracy of 0.9556 and a maximum average sensitivity of 0.7581 were achieved on DRIVE database while a maximum average accuracy of 0.9509 and a maximum average sensitivity of 0.7666 were achieved on STARE database. When compared with the previously proposed techniques on DRIVE and STARE databases, the proposed technique yields higher mean sensitivity and mean accuracy rates in the same range of very good specificity. In a related development, a non-normalized tortuosity index that combined distance metric and the vessel twist frequency proposed in this paper also achieved a strong correlation of 0.80 with the expert ground truth.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Retinal Vessel Detection and Tortuosity Measurement

Mapayi¹,

Tapamo²,

Viriri³

et al. 2016

Image Anal Stereol

Self Cite

View full text Add to dashboard Cite

show abstract

“…7 shows the comparison proposed method with method proposed by [1, 7,15] taking the first image from the DRIVE database. In this paper five different mathematical measures such as sensitivity (Sn), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV) and accuracy (Acc) are used for experimentation [34]. Sensitivity can be defined as the ability of the segmentation method to identify the number of pixels as vessel pixels.…”

Section: Experimental Evaluation and Comparisonsmentioning

confidence: 99%

Detection of retinal blood vessels from ophthalmoscope images using morphological approach

Dash

Bhoi

2017

ELCVIA

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Accurate segmentation of retinal blood vessels is an essential task for diagnosis of various pathological disorders. In this paper, a novel method has been introduced for segmenting retinal blood vessels which involves pre-processing, segmentation and post-processing. The pre-processing stage enhanced the image using contrast limited adaptive histogram equalization and 2D Gabor wavelet. The enhanced image is segmented using geodesic operators and a final segmentation output is obtained by applying a post-processing stage that involves hole filling and removal of isolated pixels. The performance of the proposed method is evaluated on the publicly available Digital retinal images for vessel extraction (DRIVE) and High-resolution fundus (HRF) databases using five different measurements and experimental analysis shows that the proposed method reach an average accuracy of 0.9541 on DRIVE database and 0.9568, 0.9478 and 0.9613 on HRF database with healthy, diabetic retinopathy (DR) and glaucomatous images respectively.

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“…To compute infinite perimeter regularization, L2 Lebesgue integral technique is employed by [11]. For retinal image preprocessing, the method of global thresholding is used [12]. For DRIVE and STARE datasets, the vessel detection by morphological component analysis (MCA) had achieved 0.9523 and 0.959 as accuracy correspondingly [13].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Segmentation Based Classification of Diabetic Retinopathy Identification using CLAHE with ResNet Model

Rajkumar¹,

Palanisamy²,

Thiruvengatanadhan³

2019

IJITEE

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Diabetic retinopathy (DR) is a widespread problem for diabetic patient and it has been a main reason for blindness in the active population. Several difficulties faced by diabetic patients because of DR can be eliminated by properly maintaining the blood glucose and by timely treatment. As the DR comes with different stages and varying difficulties, it is hard to DR and also it is time consuming. In this paper, we develop an automated segmentation based classification model for DR. Initially, the Contrast limited adaptive histogram equalization (CLAHE) is used for segmenting the images. Later, residual network (ResNet) is employed for classifying the images into different grades of DR. For experimental analysis, the dataset is derived from Kaggle website which is open source platform that attempts to build DR detection model. The highest classifier performance is attained by the presented model with the maximum accuracy of 83.78, sensitivity of 67.20 and specificity of 89.36 over compared models

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Comparative Study of Retinal Vessel Segmentation Based on Global Thresholding Techniques

Cited by 31 publications

References 35 publications

Automatic Retinal Vessel Detection and Tortuosity Measurement

Automatic Retinal Vessel Detection and Tortuosity Measurement

Detection of retinal blood vessels from ophthalmoscope images using morphological approach

An Efficient Segmentation Based Classification of Diabetic Retinopathy Identification using CLAHE with ResNet Model

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