2021
DOI: 10.1155/2021/4761517
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Blood Vessel Segmentation of Fundus Retinal Images Based on Improved Frangi and Mathematical Morphology

Abstract: An improved blood vessel segmentation algorithm on the basis of traditional Frangi filtering and the mathematical morphological method was proposed to solve the low accuracy of automatic blood vessel segmentation of fundus retinal images and high complexity of algorithms. First, a global enhanced image was generated by using the contrast-limited adaptive histogram equalization algorithm of the retinal image. An improved Frangi Hessian model was constructed by introducing the scale equivalence factor and eigenv… Show more

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Cited by 18 publications
(9 citation statements)
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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%
“…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%
“…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%
“…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%
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