Retinal blood vessels segmentation by using Gumbel probability distribution function based matched filter

Singh, Nagendra; Srivastava, Rajeev

doi:10.1016/j.cmpb.2016.03.001

Cited by 127 publications

(80 citation statements)

References 34 publications

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“…The sensitivity of this study is better than [45,[47][48][49][50][51][52] whereas its specificity is better than [53] and its accuracy and AUC is respectively more successful than [48] and [45,51] for DRIVE dataset as in Table 4. The sensitivity of this study is more successful than [45,49,51,52,54,55] whereas the performance of its specificity is better than [53,55] and its accuracy and AUC is respectively higher than [48,53,55] and [45,51] for STARE dataset as in Table 5. The performance of the sensitivity of this study is higher than [56] whereas its accuracy is more successful than [57] for CHASE_DB1 dataset as in Table 6.…”

Section: Discussionmentioning

confidence: 95%

Retinal Vessel Segmentation via Structure Tensor Coloring and Anisotropy Enhancement

Nergiz

Akın

2017

Symmetry

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Abstract:Retinal vessel segmentation is one of the preliminary tasks for developing diagnosis software systems related to various retinal diseases. In this study, a fully automated vessel segmentation system is proposed. Firstly, the vessels are enhanced using a Frangi Filter. Afterwards, Structure Tensor is applied to the response of the Frangi Filter and a 4-D tensor field is obtained. After decomposing the Eigenvalues of the tensor field, the anisotropy between the principal Eigenvalues are enhanced exponentially. Furthermore, this 4-D tensor field is converted to the 3-D space which is composed of energy, anisotropy and orientation and then a Contrast Limited Adaptive Histogram Equalization algorithm is applied to the energy space. Later, the obtained energy space is multiplied by the enhanced mean surface curvature of itself and the modified 3-D space is converted back to the 4-D tensor field. Lastly, the vessel segmentation is performed by using Otsu algorithm and tensor coloring method which is inspired by the ellipsoid tensor visualization technique. Finally, some post-processing techniques are applied to the segmentation result. In this study, the proposed method achieved mean sensitivity of 0.8123, 0.8126, 0.7246 and mean specificity of 0.9342, 0.9442, 0.9453 as well as mean accuracy of 0.9183, 0.9442, 0.9236 for DRIVE, STARE and CHASE_DB1 datasets, respectively. The mean execution time of this study is 6.104, 6.4525 and 18.8370 s for the aforementioned three datasets respectively.

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Section: Discussionmentioning

confidence: 95%

Retinal Vessel Segmentation via Structure Tensor Coloring and Anisotropy Enhancement

Nergiz

Akın

2017

Symmetry

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“…Converting the color image to gray level image with PCA also preserves the texture and color differences of the image [2]. There are several points to be taken into account and processes the image is subjected to during the conversion of the RGB image to grayscale using PCA, which are specified as follows;  Firstly, the RGB image is converted to a uniform CIELa*b* color space which is based on human color perception [14].…”

Section: Image Preprocessing and Segmentationmentioning

confidence: 99%

“…Accuracy results were obtained for DRIVE and STARE data sets as 0,9494 and 0,9610, respectively [7]. In another unsupervised segmentation realized by Sing et al, a matched filter based on the function of Gumbel probability distribution was used and the accuracy rate was found 0,9522 for the DRIVE data set and 0,9270 for the STARE data set [2]. As an example of segmentation with supervised method, Soares et al filtered the noise of the image by using 2-dimensional Gabor wavelet and thus, improved the vessel appearance.…”

Section: Introductionmentioning

confidence: 99%

“…Functional disorders resulting from such problems can be understood from the structure of the eye or the blood vessels in the retina [1]. In addition, retinal blood vessels are also important because they can provide information about pathological diseases such as hypertension and diabetes [2]. For example, changes in the diameter of the vessel and development of neovascularization indicate diabetic retinopathy, while a decrease in the ratio of the diameter of the retinal arteries to the diameter of the vessels indicates hypertension.…”

Section: Introductionmentioning

confidence: 99%

“…Another important feature of ocular blood vessels is that it is the only vein region in the human blood circulation system that can be examined from outside without requiring intervention [3]. Ophthalmologists diagnose patients by manually examining the status of blood vessels on retinal images obtained with high-resolution fundus cameras [2]. Although manual segmentation has high accuracy, it is a tedious, timeconsuming operation that can be impossible in the presence of high-volume retinal data [3].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparison of Unsupervised Segmentation of Retinal Blood Vessels in Gray Level Image with PCA and Green Channel Image

Kaya

Sarıtaş

Ceylan

2017

ijisae

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Abstract:In this study, an unsupervised retina blood vessel segmentation process was performed on the gray level images with principal component analysis (PCA) and the green channel of the RGB image, which most clearly shows retinal vessels and the results were compared. The images were improved for a good segmentation by using contrast-limited adaptive histogram equalization (CLAHE), color intensity adjustment, morphological operations and median and Gaussian filtering. Retinal vessel structures were segmented with top-hat and bot-hat morphological operations and converted to binary image by using global thresholding. The average accuracy rate obtained for the gray level image with PCA after the study was 0.9443, while the average accuracy rate obtained for the green channel was 0.9685. The study was performed using 40 images in the DRIVE data set which is one of the most common retina data sets known.

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Retinal vessel segmentation to diagnose diabetic retinopathy using fundus images: A survey

Radha

Karuna

2023

Int J Imaging Syst Tech

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Diabetes can cause damage to the retina's blood vessels in the eye leading to diabetic retinopathy (DR). The images captured using a fundus camera are used to segment and study the blood vessel damage. Once the retina is damaged, it cannot be repaired. Therefore, early detection of blood vessel damage is the only way to control the progression of the disease. It allows physicians to provide timely and appropriate treatment to patients. The ophthalmologist can manually recognize and mark these vessels based on some clinical and geometrical features; however, it is time‐consuming. Extraction and segmentation play a significant role in showing the difference between healthy and newly developed abnormal vessels. Due to the increased diabetes population, automated systems have been designed to detect retinal blood vessels and assist ophthalmologists. Identifying, extracting, and examining blood vessels are intricate processes, specifically identifying new vessels in the retina. In medical image analysis, artificial intelligence and deep learning techniques have become widely used practices for automatic retinal blood vessel segmentation. We reviewed articles from 1989 to 2023, including handcrafted segmentation to recent deep‐learning techniques with available public datasets. We have concluded this article with an overview of observed parameters, calculations, and future directions for the analysis of retinal images. We believe this review article will help researchers identify research gaps in the field of DR.

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Retinal blood vessels segmentation by using Gumbel probability distribution function based matched filter

Cited by 127 publications

References 34 publications

Retinal Vessel Segmentation via Structure Tensor Coloring and Anisotropy Enhancement

Retinal Vessel Segmentation via Structure Tensor Coloring and Anisotropy Enhancement

Comparison of Unsupervised Segmentation of Retinal Blood Vessels in Gray Level Image with PCA and Green Channel Image

Retinal vessel segmentation to diagnose diabetic retinopathy using fundus images: A survey

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