Retinal blood vessel segmentation using matched filter and Laplacian of Gaussian

Kumar, D. Vijendra; Pramanik, Ankita; Kar, Sudeshna Sil; Maity, Santi P.

doi:10.1109/spcom.2016.7746666

Cited by 26 publications

(11 citation statements)

References 17 publications

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“…The inherent zero-crossing property of Laplacian of Gaussian (LoG) filter was exploited in an algorithm proposed by Kumar et al [57] where two-dimensional matched filters with LoG kernel functions are applied to fundus retinal images to detect retinal vasculature structure which are firstly enhanced by Contrast Limited Adaptive Histogram Equalization (CLAHE) method. The proposed algorithm achieved average accuracy of 0.9626, and sensitivity and specificity of 0.7006 and 0.9871 via DRIVE dataset, respectively, and average accuracy of 0.9637, and 0.7675 and 0.9799 for sensitivity and specificity, respectively, via STARE dataset in comparison with average accuracy of 0.9340 and 0.7060 and 0.9693 for sensitivity and specificity respectively on DRIVE dataset achieved by Odstrcilik et al [47] using improved two dimensional matched filter with two-dimensional Gaussian kernel.…”

Section: Kernel-based Techniquesmentioning

confidence: 99%

Retinal Vessels Segmentation Techniques and Algorithms: A Survey

Almotiri

Elleithy

2018

Applied Sciences

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Abstract:Retinal vessels identification and localization aim to separate the different retinal vasculature structure tissues, either wide or narrow ones, from the fundus image background and other retinal anatomical structures such as optic disc, macula, and abnormal lesions. Retinal vessels identification studies are attracting more and more attention in recent years due to non-invasive fundus imaging and the crucial information contained in vasculature structure which is helpful for the detection and diagnosis of a variety of retinal pathologies included but not limited to: Diabetic Retinopathy (DR), glaucoma, hypertension, and Age-related Macular Degeneration (AMD). With the development of almost two decades, the innovative approaches applying computer-aided techniques for segmenting retinal vessels are becoming more and more crucial and coming closer to routine clinical applications. The purpose of this paper is to provide a comprehensive overview for retinal vessels segmentation techniques. Firstly, a brief introduction to retinal fundus photography and imaging modalities of retinal images is given. Then, the preprocessing operations and the state of the art methods of retinal vessels identification are introduced. Moreover, the evaluation and validation of the results of retinal vessels segmentation are discussed. Finally, an objective assessment is presented and future developments and trends are addressed for retinal vessels identification techniques.

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Section: Kernel-based Techniquesmentioning

confidence: 99%

Retinal Vessels Segmentation Techniques and Algorithms: A Survey

Almotiri

Elleithy

2018

Applied Sciences

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“…The main issue with the kernel-based filtering approaches is their tendency to highlight the non-blood vessel section of the images, which significantly degrade the overall performance of the system. Different variants of matched filtering approach are proposed in [16], [17], [18], and [19]. In [16], a hybrid matched filtering technique was proposed.…”

Section: Related Workmentioning

confidence: 99%

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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“…The authors modified the parameters of the Gaussian function to improve the overall performance of the method for identifying both thin and large blood vessel segments on the DRIVE dataset (Staal et al, 2004). Kumar et al (2016) proposed an algorithm considering twodimensional kernels and the Laplacian of Gaussian (LoG) filter for detecting the retinal vascular structure. In their algorithm, the authors apply two-dimensional matched filters with LoG kernel functions.…”

Section: Retinal Image Segmentationmentioning

confidence: 99%

Analysis of Video Retinal Angiography With Deep Learning and Eulerian Magnification

Laha

LaLonde

Carmack

et al. 2020

Front. Comput. Sci.

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The aim of this research is to present a novel computer-aided decision support tool in analyzing, quantifying, and evaluating the retinal blood vessel structure from fluorescein angiogram (FA) videos. Methods: The proposed method consists of three phases: (i) image registration for large motion removal from fluorescein angiogram videos, followed by (ii) retinal vessel segmentation, and lastly, (iii) segmentation-guided video magnification. In the image registration phase, individual frames of the video are spatiotemporally aligned using a novel wavelet-based registration approach to compensate for the global camera and patient motion. In the second phase, a capsule-based neural network architecture is employed to perform the segmentation of retinal vessels for the first time in the literature. In the final phase, a segmentation-guided Eulerian video magnification is proposed for magnifying subtle changes in the retinal video produced by blood flow through the retinal vessels. The magnification is applied only to the segmented vessels, as determined by the capsule network. This minimizes the high levels of noise present in these videos and maximizes useful information, enabling ophthalmologists to more easily identify potential regions of pathology. Results: The collected fluorescein angiogram video dataset consists of 1, 402 frames from 10 normal subjects (prospective study). Experimental results for retinal vessel segmentation show that the capsule-based algorithm outperforms a state-of-the-art convolutional neural networks (U-Net), obtaining a higher dice coefficient (85.94%) and sensitivity (92.36%) while using just 5% of the network parameters. Qualitative analysis of these videos was performed after the final phase by expert ophthalmologists, supporting the claim that artificial intelligence assisted decision support tool can be helpful for providing a better analysis of blood flow dynamics. Conclusions: The authors introduce a novel computational tool, combining a wavelet-based video registration method with a deep learning capsule-based retinal vessel segmentation algorithm and a Eulerian video magnification technique to quantitatively and qualitatively analyze FA videos. To authors' best knowledge, this is the first-ever development of such a computational tool to assist ophthalmologists with analyzing blood flow in FA videos.

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Retinal blood vessel segmentation using matched filter and Laplacian of Gaussian

Cited by 26 publications

References 17 publications

Retinal Vessels Segmentation Techniques and Algorithms: A Survey

Retinal Vessels Segmentation Techniques and Algorithms: A Survey

An Unsupervised Retinal Vessel Segmentation Using Hessian and Intensity Based Approach

Analysis of Video Retinal Angiography With Deep Learning and Eulerian Magnification

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