Retinal vessel segmentation by improved matched filtering: evaluation on a new high‐resolution fundus image database

Odstrčilík, Jan; Kolář, Radim; Budai, Attila; Hornegger, Joachim; Jan, Jiří; Gazárek, Jirí; Kuběna, Tomáš; Cernosek, P; Svoboda, O.; Angelopoulou, Elli

doi:10.1049/iet-ipr.2012.0455

Cited by 391 publications

(216 citation statements)

References 38 publications

Supporting

Mentioning

216

Contrasting

Order By: Relevance

“…All images used in this paper are obtained from the DRIONS database [3], Drive database [4], and HRF database [5]. The DRIONS database contains 110 retinal colour images of size 400 × 600 pixels showing only the OD which is located at the image centre.…”

Section: Methodsmentioning

confidence: 99%

A robust algorithm for optic disc segmentation and fovea detection in retinal fundus images

Rust

Häger

Traulsen

et al. 2017

Current Directions in Biomedical Engineering

View full text Add to dashboard Cite

Accurate optic disc (OD) segmentation and fovea detection in retinal fundus images are crucial for diagnosis in ophthalmology. We propose a robust and broadly applicable algorithm for automated, robust, reliable and consistent fovea detection based on OD segmentation. The OD segmentation is performed with morphological operations and Fuzzy C Means Clustering combined with iterative thresholding on a foreground segmentation. The fovea detection is based on a vessel segmentation via morphological operations and uses the resulting OD segmentation to determine multiple regions of interest. The fovea is determined from the largest, vesselfree candidate region. We have tested the novel method on a total of 190 images from three publicly available databases DRIONS, Drive and HRF. Compared to results of two human experts for DRIONS database, our OD segmentation yielded a dice coefficient of 0.83. Note that missing ground truth and expert variability is an issue. The new scheme achieved an overall success rate of 99.44% for OD detection and an overall success rate of 96.25% for fovea detection, which is superior to state-of-the-art approaches.

show abstract

Section: Methodsmentioning

confidence: 99%

A robust algorithm for optic disc segmentation and fovea detection in retinal fundus images

Rust

Häger

Traulsen

et al. 2017

Current Directions in Biomedical Engineering

View full text Add to dashboard Cite

show abstract

“…Their experiments show the accuracy improvement of the matched filter. The next generation of matched filter method is developed by Odstrcilik et al [53] and Fazli et al [54]. Odstrcilik et al [53] made automatic retinal blood vessel segmentation method.…”

Section: Matched Filter Methodsmentioning

confidence: 99%

Segmentation of retinal blood vessels for detection of diabetic retinopathy: A review

Aras¹,

Lestari²,

Nugroho³

et al. 2016

CST

View full text Add to dashboard Cite

Diabetic detinopathy (DR) is effect of diabetes mellitus to the human vision that is the major cause of blindness. Early diagnosis of DR is an important requirement in diabetes treatment. Retinal fundus image is commonly used to observe the diabetic retinopathy symptoms. It can present retinal features such as blood vessel and also capture the pathologies which may lead to DR. Blood vessel is one of retinal features which can show the retina pathologies. It can be extracted from retinal image by image processing with following stages: preprocessing, segmentation, and post-processing. This paper contains a review of public retinal image dataset and several methods from various conducted researches. All discussed methods are applicable to each researcher cases. There is no further analysis to conclude the best method which can be used for general cases. However, we suggest morphological and multiscale method that gives the best accuracy in segmentation.

show abstract

“…[1], Zhang et al [5], Soares et al [15], Cinsdikici et al [6], Xinge You et al [31], Budai et al [9], Azzopardi et al [10] and Odstrcilik et al [32]. The level set and region growing method [1] gives poor segmentation result for some abnormal retinal images, in [5], the MF-FDOG method loses connectivity and includes some unwanted structure.…”

Section: Experimental Evaluation and Comparisonsmentioning

confidence: 99%

Detection of retinal blood vessels from ophthalmoscope images using morphological approach

Dash

Bhoi

2017

ELCVIA

View full text Add to dashboard Cite

Accurate segmentation of retinal blood vessels is an essential task for diagnosis of various pathological disorders. In this paper, a novel method has been introduced for segmenting retinal blood vessels which involves pre-processing, segmentation and post-processing. The pre-processing stage enhanced the image using contrast limited adaptive histogram equalization and 2D Gabor wavelet. The enhanced image is segmented using geodesic operators and a final segmentation output is obtained by applying a post-processing stage that involves hole filling and removal of isolated pixels. The performance of the proposed method is evaluated on the publicly available Digital retinal images for vessel extraction (DRIVE) and High-resolution fundus (HRF) databases using five different measurements and experimental analysis shows that the proposed method reach an average accuracy of 0.9541 on DRIVE database and 0.9568, 0.9478 and 0.9613 on HRF database with healthy, diabetic retinopathy (DR) and glaucomatous images respectively.

show abstract

Retinal vessel segmentation by improved matched filtering: evaluation on a new high‐resolution fundus image database

Cited by 391 publications

References 38 publications

A robust algorithm for optic disc segmentation and fovea detection in retinal fundus images

A robust algorithm for optic disc segmentation and fovea detection in retinal fundus images

Segmentation of retinal blood vessels for detection of diabetic retinopathy: A review

Detection of retinal blood vessels from ophthalmoscope images using morphological approach

Contact Info

Product

Resources

About