Segmentation of blood vessels in fundus color images by Radon transform and morphological reconstruction

Pourreza, Reza; Pourreza, Hamidreza; Banaee, Touka

doi:10.1109/iwaci.2010.5585121

Cited by 11 publications

(5 citation statements)

References 12 publications

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“…We evaluated our algorithm in DRIVE database and compared it with MUMS-DB color database. Sensitivity of this novel method was reported and published to be more than 90%, for each of these two color databases [17].…”

Section: Discussionmentioning

confidence: 98%

Radon transform technique for linear structures detection: Application to vessel detection in fluorescein angiography fundus images

Tavakoli

Mehdizadeh

Pourreza

et al. 2011

2011 IEEE Nuclear Science Symposium Conference Record

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Ab stract-In medical images linear patterns such as blood vessels are important structures for computer-aided diagnosis and follow-up of many diseases. Moreover, image processing techniques are required to extract suitable information about vascular tree and its alteration. Analyzing of retinal blood vessel is critical work for the investigation of some diseases. In this study, we present an automated method for detecting retinal vasculatures based upon Radon transform. In preprocessing, we used top-hat transformation and averaging filter. Our main processing was included applying Radon transform, vessel certifying, and vessel refinement. Comparing the results of our method with gold standard showed that our results have more than 93% for true positive rate. In conclusion, it is possible to use Radon transform for vessel segmentation in fluorescein angiography fundus images, with acceptable sensitivity and specificity, as a necessary step in some diagnostic algorithm for retinal pathology.

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Section: Discussionmentioning

confidence: 98%

Radon transform technique for linear structures detection: Application to vessel detection in fluorescein angiography fundus images

Tavakoli

Mehdizadeh

Pourreza

et al. 2011

2011 IEEE Nuclear Science Symposium Conference Record

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Section: Discussionmentioning

confidence: 99%

“…The performance of the main step of this study, the detection of MAs, depends largely on the accuracy of vessel detection. In vessel segmentation, proposed method was tested for the first time on DRIVE database by Pourreza et al[35] and their results have been published. They also checked out on vascular tree of conjunctiva and FA retinal images by same research group in the MUMS[31,36].Obviously, the specificity of MA detection in proposed method will decrease if it works on images with poor vascular detection.As we said before, in looking toward an integrated program for automated detection of DR it is important to have adequate sensitivity.…”

mentioning

confidence: 99%

A complementary method for automated detection of microaneurysms in fluorescein angiography fundus images to assess diabetic retinopathy

Tavakoli

Shahri

Pourreza

et al. 2013

Pattern Recognition

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Early detection of microaneurysms (MAs), the first sign of Diabetic Retinopathy (DR), is an essential first step in automated detection of DR to prevent vision loss and blindness. This study presents a novel and different algorithm for automatic detection of MAs in fluorescein angiography (FA) fundus images, based on Radon transform (RT) and multi-overlapping windows. This project addresses a novel method, in detection of retinal land marks and lesions to diagnose the DR. At the first step, optic nerve head (ONH) was detected and masked. In preprocessing stage, top-hat transformation and averaging filter were applied to remove the background. In main processing section, firstly, we divided the whole preprocessed image into sub-images and then segmented and masked the vascular tree by applying RT in each sub-image. After detecting and masking retinal vessels and ONH, MAs were detected and numbered by using RT and appropriated thresholding. The results of the proposed method were evaluated reported on three different retinal images databases, the Mashhad Database with 120 FA fundus images, Second Local Database from Tehran with 50 FA retinal images and a part of Retinopathy Online Challenge (ROC) database with 22images. Automated DR detection demonstrated a sensitivity and specificity of 94% and 75% for Mashhad database and 100% and 70% for the Second Local Database respectively.

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“…Thereafter, we used a Radon transform-based algorithm to detect and extract retinal blood vessels. The detailed method has been published elsewhere 4 . Binary masks of retinal blood vessels were then used for calculation of D f .…”

Section: Methodsmentioning

confidence: 99%

Retinal vascular fractals in Behçet’s Disease: A screening method?

Norouzpour

Mehdizadeh

2015

Saudi Journal of Ophthalmology

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Segmentation of blood vessels in fundus color images by Radon transform and morphological reconstruction

Cited by 11 publications

References 12 publications

Radon transform technique for linear structures detection: Application to vessel detection in fluorescein angiography fundus images

Radon transform technique for linear structures detection: Application to vessel detection in fluorescein angiography fundus images

A complementary method for automated detection of microaneurysms in fluorescein angiography fundus images to assess diabetic retinopathy

Retinal vascular fractals in Behçet’s Disease: A screening method?

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