An approach to localize the retinal blood vessels using bit planes and centerline detection

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

doi:10.1016/j.cmpb.2011.08.009

Cited by 302 publications

(138 citation statements)

References 62 publications

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“…Bit planes based noise suppression techniques are often used for still images compression (Strutz 2001, Pandian andSivanandam 2012), and in other applications such as retinal blood vessels localization (Fraz, Barman et al 2012). Through our investigations, we found that bit planes elimination along with simple morphological operations can be utilized in foreground estimation for solar and retinal disks.…”

Section: Proposed Methodologymentioning

confidence: 86%

High Quality - Low Computational Cost Technique for Automated Principal Object Segmentation Applied in Solar and Medical Imaging

Hayajneh¹,

Rashad²,

Saraereh³

et al. 2016

CIS

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The objective of this paper is to introduce a fully computerized, simple and low-computational cost technique that can be used in the preprocessing stages of digital images. This technique is specially designed to detect the principal (largest) closed shape object that embody the useful information in certain image types and neglect and avoid other noisy objects and artifacts. The detection process starts by calculating certain statistics of the image to estimate the amount of bit-plane slicing required to exclude the non-informative and noisy background. A simple closing morphological operation is then applied and followed by circular filter applied only on the outer coarse edge to finalize the detection process. The proposed technique takes its importance from the huge explosion of images that need accurate processing in real time speedy manner. The proposed technique is implemented using MATLAB and tested on many solar and medical images; it was shown by the quantitative evaluation that the proposed technique can handle real-life (e.g. solar, medical fundus) images and shows very good potential even under noisy and artifacts conditions. Compared to the publicly available datasets, 97% and 99% of similarity detection is achieved in medical and solar images, respectively. Although it is well-know, the morphological bit-plane slicing technique is hoped to be used in the preprocessing stages of different applications to ease the subsequent image processing stages especially in real time applications where the proposed technique showed dramatic (~100 times) saving in processing time.

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Section: Proposed Methodologymentioning

confidence: 86%

High Quality - Low Computational Cost Technique for Automated Principal Object Segmentation Applied in Solar and Medical Imaging

Hayajneh¹,

Rashad²,

Saraereh³

et al. 2016

CIS

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show abstract

“…This method gets important information from shape and area of the vascular. And then, Fraz et al proposed vessel centerline detection combination and morphological bit plane slicing to extract blood vessel of retinal image [61]. This method works perfectly in feature of vessel with Gaussian Shaped Profile, but it less suitable for retinal image where arterioles are clear.…”

Section: Morphological Methodsmentioning

confidence: 99%

“…Fraz et al, in his research found a variation on intensity of the macula background to the surrounding area that may affect the segmentation process of the blood vessel. To minimize this variation, the reduction of background estimation from the original image is implemented by using 31x31 arithmetic mean kernel [22]. It was similar with L. Xu et al who reduced background estimation from the original image using 25x25 median filter for background normalization [23].…”

Section: Image Normalizationmentioning

confidence: 99%

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

Aras¹,

Lestari²,

Nugroho³

et al. 2016

CST

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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.

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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

102

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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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An approach to localize the retinal blood vessels using bit planes and centerline detection

Cited by 302 publications

References 62 publications

High Quality - Low Computational Cost Technique for Automated Principal Object Segmentation Applied in Solar and Medical Imaging

High Quality - Low Computational Cost Technique for Automated Principal Object Segmentation Applied in Solar and Medical Imaging

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

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

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