Curvelet Transform and ISODATA Thresholding for Retinal Vessel Extraction

Mahapatra, Sakambhari; Jena, Uma Ranjan; Dash, Sonali

doi:10.1007/978-981-33-4866-0_25

“…The Isodata thresholding technique is an iterative method that uses the Euclidean distance to estimate the similarity measure in the clustering of data elements into different classes. This technique has been suggested in several articles for fundus images as an effective method (20)(21)(22)(23). Most of these articles were analyzed on different types of standard databases, and less on real patients with low quality, noise, and artifacts.…”

Section: Isodata-based Segmentationmentioning

confidence: 99%

A high-accuracy segmentation Hybrid method for retinal blood vessel detection in Fluorescein Angiography images of real Diabetic Retinopathy patients

Zare,

Ramezanzadeh,

Shoeibi

et al. 2024

Preprint

0

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This study aims to develop an advanced automatic method to enhance the accuracy of blood vessel segmentation in Fundus Fluorescein angiography FFA images of patients with real Diabetic retinopathy (DR) during both early and late phases. A prospective randomized study of 280 eyes of 87 DR patients at various stages was included in this study. our approach involved using four different image enhancement techniques (Histogram equalization (HE), CLAHE, RMSHE, Proposed method (CLAHE+RMSHE)) and for each enhancement except HE, four thresholding methods including the proposed method (Modified Active Contour(MAC)+ IsoData), C-mean Fuzzy, Isodata, and MAC + otsu thresholding that are commonly used in eye fundus images across different datasets. The effectiveness of segmentation was evaluated using three metrics: Dice (early: 0.84±0.05, late:0.84±0.03), Jaccard (early:0.73±0.06, late:0.74±0.05), and BF score (early:0.98±0.02, late:0.97±0.02). We found that the RMSHE method was more effective in the late phase, while the combined CLAHE+RMSHE method worked better in the early phase. The best segmentation results for both early and late phases were achieved using a blood vessel segmentation approach based on the MAC + IsoData thresholding.

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“…The Isodata thresholding technique is an iterative method that uses the Euclidean distance to estimate the similarity measure in the clustering of data elements into different classes. This technique has been suggested in several articles for fundus images as an effective method (20)(21)(22)(23). Most of these articles were analyzed on different types of standard databases, and less on real patients with low quality, noise, and artifacts.…”

Section: Isodata-based Segmentationmentioning

confidence: 99%

A High-Accuracy Segmentation Hybrid Method For Retinal Blood Vessel Detection In Fluorescein Angiography Images Of Real Diabetic Retinopathy Patients

Ramezanzadeh,

Shoeibi,

Shariati

et al. 2024

Preprint

0

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This study aims to develop an advanced automatic method to enhance the accuracy of blood vessel segmentation in Fundus Fluorescein angiography FFA images of patients with real Diabetic retinopathy (DR) during both early and late phases. A prospective randomized study of 280 eyes of 87 DR patients at various stages was included in this study. our approach involved using four different image enhancement techniques (Histogram equalization (HE), CLAHE, RMSHE, Proposed method (CLAHE+RMSHE)) and for each enhancement except HE, four thresholding methods including the proposed method (Modified Active Contour(MAC)+ IsoData), C-mean Fuzzy, Isodata, and MAC + otsu thresholding that are commonly used in eye fundus images across different datasets. The effectiveness of segmentation was evaluated using three metrics: Dice (early: 0.84±0.05, late:0.84±0.03), Jaccard (early:0.73±0.06, late:0.74±0.05), and BF score (early:0.98±0.02, late:0.97±0.02). We found that the RMSHE method was more effective in the late phase, while the combined CLAHE+RMSHE method worked better in the early phase. The best segmentation results for both early and late phases were achieved using a blood vessel segmentation approach based on the MAC + IsoData thresholding.

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“…There are two types of machine learning-based approaches: supervised and unsupervised [35][36][37]. Supervised approaches use certain previous labeling information to determine the association of pixels with a vessel or a non-vessel.…”

Section: Introductionmentioning

confidence: 99%

Guidance Image-Based Enhanced Matched Filter with Modified Thresholding for Blood Vessel Extraction

Dash

¹

,

Verma

²

,

Kavita

³

et al. 2022

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Fundus images have been established as an important factor in analyzing and recognizing many cardiovascular and ophthalmological diseases. Consequently, precise segmentation of blood using computer vision is vital in the recognition of ailments. Although clinicians have adopted computer-aided diagnostics (CAD) in day-to-day diagnosis, it is still quite difficult to conduct fully automated analysis based exclusively on information contained in fundus images. In fundus image applications, one of the methods for conducting an automatic analysis is to ascertain symmetry/asymmetry details from corresponding areas of the retina and investigate their association with positive clinical findings. In the field of diabetic retinopathy, matched filters have been shown to be an established technique for vessel extraction. However, there is reduced efficiency in matched filters due to noisy images. In this work, a joint model of a fast guided filter and a matched filter is suggested for enhancing abnormal retinal images containing low vessel contrasts. Extracting all information from an image correctly is one of the important factors in the process of image enhancement. A guided filter has an excellent property in edge-preserving, but still tends to suffer from halo artifacts near the edges. Fast guided filtering is a technique that subsamples the filtering input image and the guidance image and calculates the local linear coefficients for upsampling. In short, the proposed technique applies a fast guided filter and a matched filter for attaining improved performance measures for vessel extraction. The recommended technique was assessed on DRIVE and CHASE_DB1 datasets and achieved accuracies of 0.9613 and 0.960, respectively, both of which are higher than the accuracy of the original matched filter and other suggested vessel segmentation algorithms.

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Curvelet Transform and ISODATA Thresholding for Retinal Vessel Extraction

Cited by 3 publications

References 11 publications

A high-accuracy segmentation Hybrid method for retinal blood vessel detection in Fluorescein Angiography images of real Diabetic Retinopathy patients

A high-accuracy segmentation Hybrid method for retinal blood vessel detection in Fluorescein Angiography images of real Diabetic Retinopathy patients

A High-Accuracy Segmentation Hybrid Method For Retinal Blood Vessel Detection In Fluorescein Angiography Images Of Real Diabetic Retinopathy Patients

Guidance Image-Based Enhanced Matched Filter with Modified Thresholding for Blood Vessel Extraction

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