A novel vessel segmentation algorithm in color images of the retina

Ocbagabir, Helen; Hameed, Isam Salah; Abdulmalik, Sarna; Buket, D. Barkana

doi:10.1109/lisat.2013.6578224

Cited by 10 publications

(10 citation statements)

References 17 publications

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“…However, the sensitivity of their supervised approach exceeds that of the 2 nd observer by quite a margin and suggests that there may be a tendency for the uncertainty in ground truth labels and test methodolgy adopted by the DRIVE database to favour supervised techniques. With this in mind, unsupervised approaches such as ours and the vessel tracing method recently proposed by Ocbagabir et al [33] perform A c c e p t e d M a n u s c r i p t…”

Section: Experimental Results and Evaluationmentioning

confidence: 99%

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Retinal vessel segmentation using multi-scale textons derived from keypoints

Zhang

Fisher

Wang

2015

Computerized Medical Imaging and Graphics

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Highlights We present a retinal vessel segmentation approach that uses textons. Vessel textons are derived from responses of a multi-scale Gabor filter bank. Page 2 of 33A c c e p t e d M a n u s c r i p tRetinal vessel segmentation using multi-scale textons derived from keypoints 2  We train on keypoint descriptors instead of labelled ground truth. We show our unsupervised approach performs well compared to previous work. Our method outperforms other unsupervised approaches on the Drive data set. AbstractThis paper presents a retinal vessel segmentation algorithm which uses a texton dictionary to classify vessel/non-vessel pixels. However, in contrast to previous work where filter parameters are learnt from manually labelled image pixels our filter parameters are derived from a smaller set of image features that we call keypoints. A Gabor filter bank, parameterised empirically by ROC analysis, is used to extract keypoints representing significant scale specific vessel features using an approach inspired by the SIFT algorithm. We first determine keypoints using a validation set and then derive seeds from these points to initialise a k-means clustering algorithm which builds a texton dictionary from another training set. During testing we use a simple 1-NN classifier to identify vessel/non-vessel pixels and evaluate our system using the DRIVE database. We achieve average values of sensitivity, specificity and accuracy of 78.12%, 96.68% and 95.05% respectively. We find that clusters of filter responses from keypoints are more robust than those derived from hand-labelled pixels. This, in turn yields textons more representative of vessel/non-vessel classes and mitigates problems arising due to intra and inter-observer variability.

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Section: Experimental Results and Evaluationmentioning

confidence: 99%

“…However although much progress has been made, vessel segmentation is still a challenging problem that remains a focus for ongoing research [14][31] [32] [33]. The primary factors that challenge the accuracy of the segmentation include:…”

Section: Introductionmentioning

confidence: 99%

Retinal vessel segmentation using multi-scale textons derived from keypoints

Zhang

Fisher

Wang

2015

Computerized Medical Imaging and Graphics

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“…Ocbagabir et al [16] developed a new algorithm called Star Networked Pixel Tracking algorithm, a rule based algorithm, for segmentation and detection of blood vessels in retinal images. Blood vessel enhancement is done using the Adaptive Histogram Equalization along with the morphological operations.…”

Section: Iiimethodsmentioning

confidence: 99%

“…[12] 0.8774 b 0.8267 a ------Lazar et al [15] 0.7650 a 0.9670 a ------Ocbagabir at al. [16] 0.7131 a 0.9824 a 0.7953 a 0.9264 a Mehrotra et. al.…”

Section: Vconclusionmentioning

confidence: 99%

A survey on blood vessel segmentation methods in retinal images

Singh

Kaur

2015

2015 International Conference on Electronic Design, Computer Networks &Amp; Automated Verification (EDCAV)

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Diabetic Retinopathy occurs in patients who suffer from diabetes for many years and as a result of which the vision gets affected. The affect can be low to severe depending on the extent to which the disease has occurred. There are two stages of the disease. The early stage is Non proliferative diabetic retinopathy (NPDR) and later is Proliferative diabetic retinopathy (PDR). In NPDR, various problems may occur such as macular edema which is swelling in the central retina and retinal ischemia which occurs due to poor blood flow. In PDR, the advanced stage of NPDR, new blood vessels starts growing in the retina known as neovascularization. The extraction of retinal blood vessels if done at early stages can be very helpful in diagnosing the severity of the disease and accordingly the treatment can be followed. In later stages, treatment is not very effective. In this paper various blood vessel segmentation techniques are discussed. Besides segmentation techniques, retinal image enhancement techniques are also discussed. The evaluation of techniques is done on publically available databases DRIVE and STARE. These databases contain retinal images along with the ground truth images which are accurately marked by the experts for the purpose of evaluation of the techniques. The paper also discusses various metrics which are frequently used for the evaluation of image segmentation techniques.

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“…The third method uses an algorithm called star networked pixel tracking algorithm that determine whether a processed pixel is part of a vessel or not. This method is used to eradicate a noise aligned in a vessel format [5].…”

Section: Introductionmentioning

confidence: 99%

A Survey on Blood Vessel Detection Methodologies in Retinal Images

Dash

Bhoi

2015

2015 International Conference on Computational Intelligence and Networks

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Automatic detection of the blood vessels in retinal images is a challenging task. In this paper a survey has been made to help biomedical engineers and medical physicists. Here we have taken three different methods for blood vessels segmentation, method (a) a novel method to segment the retinal blood vessel is used, which overcome the variations in contrast in large and thin blood vessels. Method (b) a method uses 2-D Gabor wavelet to enhance the vascular pattern and method(c) a method used is Star Networked PixelTracking Algorithm which is used to eradicate a noise aligned in a vessel format. These methods to segment the blood vessels so that we can easily diagnose and do treatment of several eye disorders.

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A novel vessel segmentation algorithm in color images of the retina

Cited by 10 publications

References 17 publications

Retinal vessel segmentation using multi-scale textons derived from keypoints

Retinal vessel segmentation using multi-scale textons derived from keypoints

A survey on blood vessel segmentation methods in retinal images

A Survey on Blood Vessel Detection Methodologies in Retinal Images

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