Blood vessel segmentation methodologies in retinal images – A survey

Fraz, Muhammad Moazam; Remagnino, Paolo; Hoppe, Andreas; Uyyanonvara, Bunyarit; Rudnicka, Alicja R.; Owen, Christopher G.; Barman, Sarah

doi:10.1016/j.cmpb.2012.03.009

Cited by 892 publications

(541 citation statements)

References 89 publications

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“…In stage (II), we further enhance the cell structures by vesselness enhancement filtering (step 3), which is used typically to enhance thin elongated structures, such as, e.g. blood vessels, in digital images (Chaudhuri et al, 1989;Zhang et al, 2010;Fraz et al, 2012). Here, we apply a filter kernel, as proposed in Sofka and Stewart (2006).…”

Section: Cell Boundary Segmentation and Region Filteringmentioning

confidence: 99%

PaCeQuant: A Tool for High-Throughput Quantification of Pavement Cell Shape Characteristics

et al. 2017

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Section: Cell Boundary Segmentation and Region Filteringmentioning

confidence: 99%

PaCeQuant: A Tool for High-Throughput Quantification of Pavement Cell Shape Characteristics

et al. 2017

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“…An accurate segmentation of retinal blood vessels is thus a crucial step and improves the detection of retinal lesions [19]. Several methods have been proposed for the detection and removal of these structures [20,21].…”

Section: Introductionmentioning

confidence: 99%

Automated detection of microaneurysms using scale-adapted blob analysis and semi-supervised learning

Adal¹,

Sidibé²,

Ali³

et al. 2014

Computer Methods and Programs in Biomedicine

103

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Despite several attempts, automated detection of microaneurysm (MA) from digital fundus images still remains to be an open issue. This is due to the subtle nature of MAs against the surrounding tissues. In this paper, the microaneurysm detection problem is modeled as finding interest regions or blobs from an image and an automatic local-scale selection technique is presented. Several scale-adapted region descriptors are then introduced to characterize these blob regions. A semi-supervised based learning approach, which requires few manually annotated learning examples, is also proposed to train a classifier to detect true MAs. The developed system is built using only few manually labeled and a large number of unlabeled retinal color fundus images. The performance of the overall system is evaluated on Retinopathy Online Challenge (ROC) competition database. A competition performance measure (CPM) of 0.364 shows the competitiveness of the proposed system against state-of-the art techniques as well as the applicability of the proposed features to analyze fundus images.

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“… Database consists of three groups: 59 images of infected retinas under diabetes group, 61 images of control group and 92 images of macular degeneration group based on age (Fraz et al, 2012).…”

Section: Aria Onlinementioning

confidence: 99%

Wavelet Filter techniques for segmenting retinal blood vessels

Al-Marghilnai

Mansour

2017

Int. j. adv. appl. sci.

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Retinal fundus image is generally used to examine the diabetic retinopathy symptoms, by analysing blood vessel segmentation and also access the pathologies of the eye. Retinal blood vessel details can be mined from retinal fundus images through image processing. Processing involves three stages, Pre-processing, Segmentation, and Post-processing. Among the different segmentation algorithms existing, the wavelet filter method has been shown to be highly advantageous in distinguishing blood vessels effectively. Under this method, the objects in noisy background can be segmented and hence sort out the image from the background in a finer way. Retinal images obtained from retinal databases like DRIVE and STARE can be analysed using the wavelet filter algorithm. Herein wavelet filter method will be evaluated for efficient segmentation of the retinal blood vessels using retinal images obtained from databases.

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Blood vessel segmentation methodologies in retinal images – A survey

Cited by 892 publications

References 89 publications

PaCeQuant: A Tool for High-Throughput Quantification of Pavement Cell Shape Characteristics

PaCeQuant: A Tool for High-Throughput Quantification of Pavement Cell Shape Characteristics

Automated detection of microaneurysms using scale-adapted blob analysis and semi-supervised learning

Wavelet Filter techniques for segmenting retinal blood vessels

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