Blood Vessel Segmentation of Fundus Retinal Images Based on Improved Frangi and Mathematical Morphology

Feng, Tian; Li, Ying; Wang, Jing; Chen, Wei

doi:10.1155/2021/4761517

Cited by 18 publications

(9 citation statements)

References 35 publications

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“…Similarly, we achieved an accuracy of 0.9560 on the STARE dataset, which is the second highest (the highest being the method of Upadhyay et al [38]). Additionally, among the unsupervised category, the proposed system achieves the second highest specificity after Tian et al's method [41]. However, with the sensitivity of 0.7 or more the proposed system has the highest specificity among all the methods described under supervised and unsupervised categories on the DRIVE dataset, as shown in Table 4.…”

Section: Resultsmentioning

confidence: 85%

Multifilters-Based Unsupervised Method for Retinal Blood Vessel Segmentation

Muzammil,

Shah,

Shahzad

et al. 2022

Applied Sciences

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Fundus imaging is one of the crucial methods that help ophthalmologists for diagnosing the various eye diseases in modern medicine. An accurate vessel segmentation method can be a convenient tool to foresee and analyze fatal diseases, including hypertension or diabetes, which damage the retinal vessel’s appearance. This work suggests an unsupervised approach for vessels segmentation out of retinal images. The proposed method includes multiple steps. Firstly, from the colored retinal image, green channel is extracted and preprocessed utilizing Contrast Limited Histogram Equalization as well as Fuzzy Histogram Based Equalization for contrast enhancement. To expel geometrical articles (macula, optic disk) and noise, top-hat morphological operations are used. On the resulted enhanced image, matched filter and Gabor wavelet filter are applied, and the outputs from both is added to extract vessels pixels. The resulting image with the now noticeable blood vessel is binarized using human visual system (HVS). A final image of segmented blood vessel is obtained by applying post-processing. The suggested method is assessed on two public datasets (DRIVE and STARE) and showed comparable results with regard to sensitivity, specificity and accuracy. The results we achieved with respect to sensitivity, specificity together with accuracy on DRIVE database are 0.7271, 0.9798 and 0.9573, and on STARE database these are 0.7164, 0.9760, and 0.9560, respectively, in less than 3.17 s on average per image.

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Section: Resultsmentioning

confidence: 85%

Multifilters-Based Unsupervised Method for Retinal Blood Vessel Segmentation

Muzammil,

Shah,

Shahzad

et al. 2022

Applied Sciences

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“…The extraction of vascular similarity features involved the vascular similarity function, which was first proposed in 1998 and was widely used in the field of vascular segmentation [ 26 ]. Nevertheless, most of them were used for vascular segmentation of retinal images with high segmentation accuracy [ 27 , 28 ]. Then, the improved region growth segmentation algorithm was compared with the traditional region segmentation algorithm.…”

Section: Discussionmentioning

confidence: 99%

Diagnostic Value of Coronary Computed Tomography Angiography Image under Automatic Segmentation Algorithm for Restenosis after Coronary Stenting

He¹,

Zhao²,

Xu³

et al. 2022

Contrast Media & Molecular Imaging

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The diagnostic efficacy of coronary computed tomography angiography (CTA) images of coronary arteries in restenosis after coronary stenting based on the combination of the convolutional neural network (CNN) algorithm and the automatic segmentation algorithm for region growth of vascular similarity features was explored to provide a more effective diagnostic method for patients. 130 patients with coronary artery disease were randomly selected as the research objects, and they were averagely classified into the control group (conventional coronary CTA image diagnosis) and the observation group (coronary CTA image diagnosis based on an improved automatic segmentation algorithm). Based on the diagnostic criteria of coronary angiography (CAG), the efficacy of two kinds of coronary CTA images on the postoperative subsequent visit of coronary heart disease (CHD) stenting was evaluated. The results showed that the accuracy of the CNN algorithm was 87.89%, and the average voxel error of the improved algorithm was signally lower than that of the traditional algorithm (1.8921 HU/voxel vs. 7.10091 HU/voxel) ( p < 0.05). The average score of the coronary CTA image in the observation group was higher than that in the control group (2.89 ± 0.11 points vs. 2.01 ± 0.73 points) ( p < 0.05). The diagnostic sensitivity (91.43%), specificity (86.76%), positive predictive value (88.89%), negative predictive value (89.66%), and accuracy (89.23%) of the observation group were higher than those of the control group ( p < 0.05). In conclusion, the region growth algorithm under the CNN algorithm and vascular similarity features had an accurate segmentation effect, which was helpful for the diagnosis of CTA image in restenosis after coronary stenting.

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“…Contras Limited Adaptive Histogram Equalization or CLAHE is a technique to rise image contrast. The CLAHE method can be used uniformly to increase the image contrast and reduce noise on image [7], [8]. Several studies that used CLAHE to improve image contrast, including Shahid and Taj [4], Bahadar Khan, Khaliq and Shahid [5], and Ravichandran and Raja [9], produced good retinal blood vessel segmentation.…”

Section: Introductionmentioning

confidence: 99%

“…Various segmentation methods have been developed, Otsu Thresholding is an efficient and easy segmentation method because the method uses local threshold values [7], [14]. Otsu Thresholding uses a different threshold at each pixel level that is in a partitioned sub-image of an image.…”

Section: Introductionmentioning

confidence: 99%

“…This method has the advantage of working better on enhanced images and performing simple computational calculations [15]. Several studies that have applied the Otsu Thresholding method for blood vessel segmentation include Tian et al [7] used Otsu Thresholding with image improvement using Frangi Hessian and Improved mathematical morphology, resulting in accuracy and specificity of 95.54% and 98.02%, respectively, but the sensitivity value produced was still low at 69.42%. Ali and Muhammad [16] used Otsu Thresholding to improve the image using the Frangi filter, resulting in 96.99% accuracy and 97.28% specificity, but the sensitivity value remained low at 63.99%.…”

Section: Introductionmentioning

confidence: 99%

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Contrast enhancement for improved blood vessels retinal segmentation using top-hat transformation and otsu thresholding

Arhami¹,

Desiani

Yahdin

et al. 2022

Int. J. Adv. Intell. Informatics

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Diabetic Retinopathy is a effect of diabetes. It results abnormalities in the retinal blood vessels. The abnormalities can cause blurry vision and blindness. Automatic retinal blood vessels segmentation on retinal image can detect abnormalities in these blood vessels, actually resulting in faster and more accurate segmentation results. The paper proposed an automatic blood vessel segmentation method that combined Otsu Thresholding with image enhancement techniques. In image enhancement, it combined CLAHE with Top-hat transformation to improve image quality. The study used DRIVE dataset that provided retinal image data. The image data in dataset was generated by the fundus camera. The CLAHE and Top-hat transformation methods were applied to rise the contrast and reduce noise on the image. The images that had good quality could help the segmentation process to find blood vessels in retinal images appropriately by a computer. It improved the performance of the segmentation method for detecting blood vessels in retinal image. Otsu Thresholding was used to segment blood vessel pixels and other pixels as background by local threshold. To evaluation performance of the proposed method, the study has been measured accuracy, sensitivity, and specificity. The DRIVE dataset's study results showed that the averages of accuracy, sensitivity, and specificity values were 94.7%, 72.28%, and 96.87%, respectively. It indicated that the proposed method was successful and well to work on blood vessels segmentation retinal images especially for thick blood vessels.

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Blood Vessel Segmentation of Fundus Retinal Images Based on Improved Frangi and Mathematical Morphology

Cited by 18 publications

References 35 publications

Multifilters-Based Unsupervised Method for Retinal Blood Vessel Segmentation

Multifilters-Based Unsupervised Method for Retinal Blood Vessel Segmentation

Diagnostic Value of Coronary Computed Tomography Angiography Image under Automatic Segmentation Algorithm for Restenosis after Coronary Stenting

Contrast enhancement for improved blood vessels retinal segmentation using top-hat transformation and otsu thresholding

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