RETRACTED ARTICLE: Noise-estimation-based anisotropic diffusion approach for retinal blood vessel segmentation

Abdallah, Mariem Ben; Azar, Ahmad Taher; Guedri, Hichem; Malek, Jihene; Belmabrouk, Hafedh

doi:10.1007/s00521-016-2811-9

Cited by 18 publications

(7 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The mean accuracy values of (94.01%, 94.10%, 94.92%, 95.45% and 94.80%) from [96], [97], [81], [77] and [95] respectively are lower than the mean accuracy value of 94.21 per cent from the adaptive window size of 15, but higher than the mean accuracy value of 93.98 per cent from the adaptive window size of 17. The average sensitivity values of 74.58 per cent from the adaptive window size of 15 and the accuracy value of 78.48% from the adaptive window size of 17 are higher than (67.34%, 67.86%, 72.48%, 71.40%, 68.01% and 64.33%) from [55], [80], [81], [77], [91] and [90] respectively.…”

Section: Comparative Analysis Of Proposed Bv Segmentation Model and E...mentioning

confidence: 80%

See 1 more Smart Citation

Optic Disc and Vessel Segmentation in Retinal Images using Correlation Texture

Fatoki,

Owolawi,

Mapayi

2023

iCARTi

View full text Add to dashboard Cite

Globally, glaucoma has been recognized as the secondmost common reason for blindness. This affects the anatomy of the eyes, including blood vessels (BVs) and optic disc (OD). State-of-the-art literature has revealed that this disease is the root cause of degeneration in the OD, with some effects on the morphology of the BVs in the retina. These can result in total blindness if left untreated. Previous studies have shown that to effectively diagnose a disease, it is necessary to automatically detect the OD and the BVs with high accuracy in the fundus image. Segmentation of OD and BVs has, however, been challenging, due to the complex and poor segmentation performances. Such performances arise from the intricacies of vascular network width variation, illumination of images, and the structure of retinal images. Examples are retinal image boundaries on ODs and retinal lesions, due to disease. Hence the need for an investigation of the appropriate image feature(s) that will facilitate improvements to OD and BV segmentation performances. Texture is an important indicator used in image analysis for object segmentation. This work proposed the use of image-correlation texture information computational approaches for the segmentation of BVs, using variant window sizes; and OD using grayscale and green channel in the retinal images. This study revealed that the segmentation of BVs using variant window sizes (15x15) and (17x17) achieved high accuracy values of 95.11 per cent and 94.93 per cent; with accuracy values of 94.21 per cent and 93.98 per cent revealed in the DRIVE and STARE databases, respectively. Usually, sensitivity values of 74.84 per cent and 75.94 per cent, with average sensitivity values of 74.58 percent and 78.48 per cent, are achieved BV segmentation on the window sizes (15x15) and (17x17), indicated in DRIVE and STARE databases, respectively. Also, an average specificity value of 97.08 per cent and 96.77 per cent with average specificity values of 95.77 per cent and 95.18 per cent are achieved for BV segmentation on the window sizes (15x15) and (17x17), on DRIVE and STARE databases, respectively. Further, the segmentation of OD using grayscale achieved a slightly greater accuracy rate (98.59%) compared to the accuracy rate (98.36%) realized on green channel. This study also showed that the proposed approaches for BV and OD segmentation in this research achieved better results compared with several existing methods in the literature.

show abstract

Section: Comparative Analysis Of Proposed Bv Segmentation Model and E...mentioning

confidence: 80%

“…window size of 15 and 96.77 per cent from segmented BVs with window size of 17 are higher than the average specificity value of 95.79 per cent from [80]. 93.79% and 93.54% achieved by [88], [89], [55], [90], [91], [92], [93], [80] and the second human observer [55] respectively.…”

Section: Comparative Analysis Of Proposed Bv Segmentation Model and E...mentioning

confidence: 92%

Optic Disc and Vessel Segmentation in Retinal Images using Correlation Texture

Fatoki,

Owolawi,

Mapayi

2023

iCARTi

View full text Add to dashboard Cite

show abstract

“…Year Sensitivity Specificity Accuracy 2nd human observer Not available 0.8952 0.9385 0.9349 Hoover et al [8] 2000 0.6734 0.9568 0.9267 Jiang and Mojon [33] 2003 Not available Not available 0.9009 Martinez-Perez et al [31] 2007 0.7506 0.9569 0.9410 Al-Rawi et al [29] 2007 Not available Not available 0.9090 Palomera-Pérez et al [32] 2010 0.769 0.944 0.926 Budai et al [30] 2013 0.5800 0.9820 0.9370 Chakraborti et al [2] 2014 0.6786 0.9586 0.9379 Soomro et al [20] 2017 0.7480 0.9220 0.9480 Abdallah et al [17] 2018 0.6801 0.9711 0.9388 Leopold et al [21] 2019 0.6433 0.9472 0.9045 Proposed method 2019 0.7581 0.9550 0.9401…”

Section: Methodsmentioning

confidence: 99%

“…Vessel tracking presented in [15,16] uses the profile model, guided by local information, to follow the path which best matches a vessel and segment it incrementally. Abdallah et al in [17] applied adaptive noise-reducing anisotropic diffusion filter and multiscale line-tracking algorithm to the retinal vessel extraction process. Supervised methods for retinal vessel segmentation use vessel data to train a classifier to identify whether a pixel is vessel or nonvessel, such as support vector machine-based methods and neural network-based methods.…”

Section: Introductionmentioning

confidence: 99%

An Effective Retinal Blood Vessel Segmentation by Using Automatic Random Walks Based on Centerline Extraction

Gao

Chen

Lin

2020

BioMed Research International

View full text Add to dashboard Cite

The retinal blood vessel analysis has been widely used in the diagnoses of diseases by ophthalmologists. According to the complex morphological characteristics of the blood vessels in normal and abnormal images, an automatic method by using the random walk algorithms based on the centerlines is proposed to segment retinal blood vessels. Hessian-based multiscale vascular enhancement filtering is used to display the vessel structures in maximum intensity projection. Random walk algorithm provides a unique and quality solution, which is robust to weak object boundaries. Seed groups in the random walk segmentation are labeled according to the centerlines, which are extracted by using the divergence of the normalized gradient vector field and the morphological method. Experiments of the proposed method are implemented on the publicly available STARE (the Structured Analysis of the Retina) database. The results are compared to other existing retinal blood vessel segmentation methods with respect to the accuracy, sensitivity, and specificity, and the proposed method is proved to be more sensitive in detecting the retinal blood vessels in both normal and pathological areas.

show abstract

“…The gradient anisotropic diffusion was optimized to generate the possible heat source in an aluminum plate. Ben Abdallah, et al [71] developed a segmentation technique for blood vessel images based on anisotropic diffusion. To remove the noise in the RGB fundus images, an adaptive anisotropic diffusion filter was used, with the combination of a noise level functions.…”

Section: Anisotropic Diffusion Filtering For Denoisingmentioning

confidence: 99%