An Unsupervised Approach for Extraction of Blood Vessels from Fundus Images

Dash, Jyotiprava; Bhoi, Nilamani

doi:10.1007/s10278-018-0059-x

Cited by 28 publications

(20 citation statements)

References 46 publications

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“…It is pertinent to note here that [44] and [16] achieved such high sensitivity at the cost of specificity and accuracy. Our obtained specificity is slightly lower than the specificity of [46], [47], and [48] which are the best, the second best and third best among unsupervised methods respectively. The average accuracy of the proposed method lies in fourth place close behind [23] who achieved third-best accuracy at the cost of sensitivity.…”

Section: B Comparison With State-of-the-artcontrasting

confidence: 56%

“…The average accuracy of the proposed method lies in fourth place close behind [23] who achieved third-best accuracy at the cost of sensitivity. Talking about MCC, our method outperforms [46] and [23] who achieved best specificity and third-best accuracy respectively. The MCC of our method is in second-place close behind [25].…”

Section: B Comparison With State-of-the-artmentioning

confidence: 88%

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An Unsupervised Retinal Vessel Segmentation Using Hessian and Intensity Based Approach

2020

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The structure of blood vessels play a crucial role in diagnoses of the various vision threatening diseases including Glaucoma and Diabetic Retinopathy (DR). The correct segmentation of retinal blood vessels is a crucial step in the study of retinal fundus images. We proposed a simple unsupervised approach by using a combination of Hessian based approach and intensity transformation approach. We have applied CLAHE for enhancing the contrast of the retinal fundus images. An enhanced version of PSO algorithm is applied for contextual region tuning of CLAHE. Morphological filter and Wiener filter are used to denoise the enhanced image. The eigenvalues are obtained from the Hessian matrix at two different scales to extract thick and thin vessel enhanced images separately. The intensity transformation approach is separately applied to the enhanced image to maximize the vessel details. Global Otsu thresholding is applied on intensity transformed image and thick vessel enhanced image whereas ISODATA local thresholding is applied on thin vessel enhanced image. Finally, a simple post-processing step based on the region parameters such as area, eccentricity, and solidity is used. The region parameters are obtained for each connected component in input binary images. The threshold values of region parameters are empirically investigated and applied to each of the three binary images to remove the non-vessel components. The thresholded images are combined by applying logical OR operator, which resulted in the final segmented binary image. We assessed our developed framework on the open-access CHASE_DB1 and DRIVE datasets, achieving a sensitivity of 0.7776 and 0.7851, and an accuracy of 0.9505 and 0.9559 respectively. These results outperform several state-of-the-art unsupervised methods. The reduced computational complexity and significantly improved evaluation metrics advocates for its use in the automated diagnostic systems for retinal image analysis.

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Section: B Comparison With State-of-the-artcontrasting

confidence: 56%

Section: B Comparison With State-of-the-artmentioning

confidence: 88%

An Unsupervised Retinal Vessel Segmentation Using Hessian and Intensity Based Approach

2020

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“…Also, the AUC reported in [33] is less than the AUC obtained by our method. We have also included unsupervised methods [45] [46] [47] in our comparison to comprehensively analyze the performance of our method.…”

Section: Comparison With Previous Methodsmentioning

confidence: 99%

A supervised blood vessel segmentation technique for digital Fundus images using Zernike Moment based features

et al. 2020

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This paper proposes a new supervised method for blood vessel segmentation using Zernike moment-based shape descriptors. The method implements a pixel wise classification by computing a 11-D feature vector comprising of both statistical (gray-level) features and shape-based (Zernike moment) features. Also the feature set contains optimal coefficients of the Zernike Moments which were derived based on the maximum differentiability between the blood vessel and background pixels. A manually selected training points obtained from the training set of the DRIVE dataset, covering all possible manifestations were used for training the ANN-based binary classifier. The method was evaluated on unknown test samples of DRIVE and STARE databases and returned accuracies of 0.945 and 0.9486 respectively, outperforming other existing supervised learning methods. Further, the segmented outputs were able to cover thinner blood vessels better than previous methods, aiding in early detection of pathologies.

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“…The DC is defined as the harmonic mean of PPV and Se. The accuracy is the amount of competence to categorize the quantity of conformism of the segmented image to the manually segmented image [40], [41].…”

Section: Resultsmentioning

confidence: 99%

“…Parida et al / Electronic Letters on Computer Vision and ImageAnalysis 19(1):[38][39][40][41][42][43][44][45][46][47][48][49][50][51][52] 2020 …”

mentioning

confidence: 99%

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

Parida

Dash

Bhoi³

2020

ELCVIA

Self Cite

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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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An Unsupervised Approach for Extraction of Blood Vessels from Fundus Images

Cited by 28 publications

References 46 publications

An Unsupervised Retinal Vessel Segmentation Using Hessian and Intensity Based Approach

An Unsupervised Retinal Vessel Segmentation Using Hessian and Intensity Based Approach

A supervised blood vessel segmentation technique for digital Fundus images using Zernike Moment based features

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

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