Blood vessel segmentation in color fundus images based on regional and Hessian features

Shah, Syed Ayaz Ali; Tang, Tong Boon; Faye, Ibrahima; Laude, Augustinus

doi:10.1007/s00417-017-3677-y

Cited by 36 publications

(15 citation statements)

References 30 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%

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%

An Unsupervised Retinal Vessel Segmentation Using Hessian and Intensity Based Approach

2020

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“…To this end, different strategies have been devised. The strategies can be roughly grouped into i) multiscale, ii) matched filtering, iii) mathematical morphology, iv) hierarchical, v) model and vi) deep learning approach [1]. Furthermore, they can also be categorized into supervised and unsupervised algorithm.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Method for Retinal Vessel Segmentation Based on Gabor Wavelet and Multiscale Line Detector

et al. 2019

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Eye and systemic diseases are known to manifest themselves in retinal vasculature. Segmentation of retinal vessel is one of the important steps in retinal image analysis. A simple unsupervised method based on Gabor wavelet and Multiscale Line Detector is proposed for retinal vessel segmentation. Vessels are enhanced by linear superposition of first scale Gabor wavelet image and complemented Green channel. Multiscale Line Detector is used to segment the blood vessels. Finally, a simple post processing scheme based on median filtering is deployed to remove false positives. The proposed scheme was evaluated with publicly available datasets called DRIVE, STARE and HRF, obtaining an accuracy of 0.9470, 0.9472, and 0.9559, and a sensitivity of 0.7421, 0.8004, and 0.7207, respectively. These results are comparable to the state-of-the-art methods, albeit with a simpler approach. INDEX TERMS Blood vessel segmentation, color retinal images, Gabor wavelet, line detector, image processing, unsupervised method, image preprocessing.

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“…While, Boykov et al provided the graph cut algorithm which could take into account both the density of each pixel and the connectivity between the pixels, making the segmentation process more intellegent and accurate than before [19,20]. In recent years, the graph cut algorithms have been applied to segment specific tissues in medical image, such as liver, artery wall and lung tumor, as well as the analysis of ocular fundus images [21][22][23][24][25][26][27]. The aims of this study were to introduce an interactive segmentation method based on graph cut algorithm and to evaluate the accuracy and reliability of the method for orbital soft tissue segmentation and clinical reconstruction.…”

Section: Introductionmentioning

confidence: 99%

An accurate interactive segmentation and volume calculation of orbital soft tissue for orbital reconstruction after enucleation

Ning

Gao

et al. 2019

BMC Ophthalmol

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Background: Accurate measurement and reconstruction of orbital soft tissue is important to diagnosis and treatment of orbital diseases. This study applied an interactive graph cut method to orbital soft tissue precise segmentation and calculation in computerized tomography (CT) images, and to estimate its application in orbital reconstruction. Methods: The interactive graph cut method was introduced to segment extraocular muscle and intraorbital fat in CT images. Intra-and inter-observer variability of tissue volume measured by graph cut segmentation was validated. Accuracy and reliability of the method was accessed by comparing with manual delineation and commercial medical image software. Intraorbital structure of 10 patients after enucleation surgery was reconstructed based on graph cut segmentation and soft tissue volume were compared within two different surgical techniques. Results: Both muscle and fat tissue segmentation results of graph cut method showed good consistency with ground truth in phantom data. There were no significant differences in muscle calculations between observers or segmental methods (p > 0.05). Graph cut results of fat tissue had coincidental variable trend with ground truth which could identify 0.1cm 3 variation. The mean performance time of graph cut segmentation was significantly shorter than manual delineation and commercial software (p < 0.001). Jaccard similarity and Dice coefficient of graph cut method were 0.767 ± 0.045 and 0.836 ± 0.032 for human normal extraocular muscle segmentation. The measurements of fat tissue were significantly better in graph cut than those in commercial software (p < 0.05). Orbital soft tissue volume was decreased in post-enucleation orbit than that in normal orbit (p < 0.05). Conclusion: The graph cut method was validated to have good accuracy, reliability and efficiency in orbit soft tissue segmentation. It could discern minor volume changes of soft tissue. The interactive segmenting technique would be a valuable tool for dynamic analysis and prediction of therapeutic effect and orbital reconstruction.

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Blood vessel segmentation in color fundus images based on regional and Hessian features

Cited by 36 publications

References 30 publications

An Unsupervised Retinal Vessel Segmentation Using Hessian and Intensity Based Approach

An Unsupervised Retinal Vessel Segmentation Using Hessian and Intensity Based Approach

Unsupervised Method for Retinal Vessel Segmentation Based on Gabor Wavelet and Multiscale Line Detector

An accurate interactive segmentation and volume calculation of orbital soft tissue for orbital reconstruction after enucleation

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