Iterative Vessel Segmentation of Fundus Images

This paper presents a novel unsupervised iterative blood vessel segmentation algorithm using fundus images. First, a vessel enhanced image is generated by tophat reconstruction of the negative green plane image. An initial estimate of the segmented vasculature is extracted by global thresholding the...

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Vydáno v:	IEEE transactions on biomedical engineering Ročník 62; číslo 7; s. 1738 - 1749
Hlavní autoři:	Roychowdhury, Sohini, Koozekanani, Dara D., Parhi, Keshab K.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.07.2015
Témata:	accuracy Algorithms Biomedical imaging Blood vessels computational complexity Databases, Factual Diagnostic Techniques, Ophthalmological fundus image Fundus Oculi Humans Image Processing, Computer-Assisted - methods Image segmentation iterative algorithm Iterative algorithms morphological reconstruction Observers Pathology Retina Retinal Vessels - anatomy & histology ROC Curve stopping criterion Vessel segmentation Accuracy vessel segmentation morphological reconstruction stopping criterion fundus image iterative algorithm computational complexity
ISSN:	0018-9294, 1558-2531, 1558-2531
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Abstract	This paper presents a novel unsupervised iterative blood vessel segmentation algorithm using fundus images. First, a vessel enhanced image is generated by tophat reconstruction of the negative green plane image. An initial estimate of the segmented vasculature is extracted by global thresholding the vessel enhanced image. Next, new vessel pixels are identified iteratively by adaptive thresholding of the residual image generated by masking out the existing segmented vessel estimate from the vessel enhanced image. The new vessel pixels are, then, region grown into the existing vessel, thereby resulting in an iterative enhancement of the segmented vessel structure. As the iterations progress, the number of false edge pixels identified as new vessel pixels increases compared to the number of actual vessel pixels. A key contribution of this paper is a novel stopping criterion that terminates the iterative process leading to higher vessel segmentation accuracy. This iterative algorithm is robust to the rate of new vessel pixel addition since it achieves 93.2-95.35% vessel segmentation accuracy with 0.9577-0.9638 area under ROC curve (AUC) on abnormal retinal images from the STARE dataset. The proposed algorithm is computationally efficient and consistent in vessel segmentation performance for retinal images with variations due to pathology, uneven illumination, pigmentation, and fields of view since it achieves a vessel segmentation accuracy of about 95% in an average time of 2.45, 3.95, and 8 s on images from three public datasets DRIVE, STARE, and CHASE_DB1, respectively. Additionally, the proposed algorithm has more than 90% segmentation accuracy for segmenting peripapillary blood vessels in the images from the DRIVE and CHASE_DB1 datasets.
AbstractList	This paper presents a novel unsupervised iterative blood vessel segmentation algorithm using fundus images. First, a vessel enhanced image is generated by tophat reconstruction of the negative green plane image. An initial estimate of the segmented vasculature is extracted by global thresholding the vessel enhanced image. Next, new vessel pixels are identified iteratively by adaptive thresholding of the residual image generated by masking out the existing segmented vessel estimate from the vessel enhanced image. The new vessel pixels are, then, region grown into the existing vessel, thereby resulting in an iterative enhancement of the segmented vessel structure. As the iterations progress, the number of false edge pixels identified as new vessel pixels increases compared to the number of actual vessel pixels. A key contribution of this paper is a novel stopping criterion that terminates the iterative process leading to higher vessel segmentation accuracy. This iterative algorithm is robust to the rate of new vessel pixel addition since it achieves 93.2-95.35% vessel segmentation accuracy with 0.9577-0.9638 area under ROC curve (AUC) on abnormal retinal images from the STARE dataset. The proposed algorithm is computationally efficient and consistent in vessel segmentation performance for retinal images with variations due to pathology, uneven illumination, pigmentation, and fields of view since it achieves a vessel segmentation accuracy of about 95% in an average time of 2.45, 3.95, and 8 s on images from three public datasets DRIVE, STARE, and CHASE_DB1, respectively. Additionally, the proposed algorithm has more than 90% segmentation accuracy for segmenting peripapillary blood vessels in the images from the DRIVE and CHASE_DB1 datasets. This paper presents a novel unsupervised iterative blood vessel segmentation algorithm using fundus images. First, a vessel enhanced image is generated by tophat reconstruction of the negative green plane image. An initial estimate of the segmented vasculature is extracted by global thresholding the vessel enhanced image. Next, new vessel pixels are identified iteratively by adaptive thresholding of the residual image generated by masking out the existing segmented vessel estimate from the vessel enhanced image. The new vessel pixels are, then, region grown into the existing vessel, thereby resulting in an iterative enhancement of the segmented vessel structure. As the iterations progress, the number of false edge pixels identified as new vessel pixels increases compared to the number of actual vessel pixels. A key contribution of this paper is a novel stopping criterion that terminates the iterative process leading to higher vessel segmentation accuracy. This iterative algorithm is robust to the rate of new vessel pixel addition since it achieves 93.2-95.35% vessel segmentation accuracy with 0.9577-0.9638 area under ROC curve (AUC) on abnormal retinal images from the STARE dataset. The proposed algorithm is computationally efficient and consistent in vessel segmentation performance for retinal images with variations due to pathology, uneven illumination, pigmentation, and fields of view since it achieves a vessel segmentation accuracy of about 95% in an average time of 2.45, 3.95, and 8 s on images from three public datasets DRIVE, STARE, and CHASE_DB1, respectively. Additionally, the proposed algorithm has more than 90% segmentation accuracy for segmenting peripapillary blood vessels in the images from the DRIVE and CHASE_DB1 datasets.This paper presents a novel unsupervised iterative blood vessel segmentation algorithm using fundus images. First, a vessel enhanced image is generated by tophat reconstruction of the negative green plane image. An initial estimate of the segmented vasculature is extracted by global thresholding the vessel enhanced image. Next, new vessel pixels are identified iteratively by adaptive thresholding of the residual image generated by masking out the existing segmented vessel estimate from the vessel enhanced image. The new vessel pixels are, then, region grown into the existing vessel, thereby resulting in an iterative enhancement of the segmented vessel structure. As the iterations progress, the number of false edge pixels identified as new vessel pixels increases compared to the number of actual vessel pixels. A key contribution of this paper is a novel stopping criterion that terminates the iterative process leading to higher vessel segmentation accuracy. This iterative algorithm is robust to the rate of new vessel pixel addition since it achieves 93.2-95.35% vessel segmentation accuracy with 0.9577-0.9638 area under ROC curve (AUC) on abnormal retinal images from the STARE dataset. The proposed algorithm is computationally efficient and consistent in vessel segmentation performance for retinal images with variations due to pathology, uneven illumination, pigmentation, and fields of view since it achieves a vessel segmentation accuracy of about 95% in an average time of 2.45, 3.95, and 8 s on images from three public datasets DRIVE, STARE, and CHASE_DB1, respectively. Additionally, the proposed algorithm has more than 90% segmentation accuracy for segmenting peripapillary blood vessels in the images from the DRIVE and CHASE_DB1 datasets.
Author	Koozekanani, Dara D. Parhi, Keshab K. Roychowdhury, Sohini
Author_xml	– sequence: 1 givenname: Sohini surname: Roychowdhury fullname: Roychowdhury, Sohini email: roych@uw.edu organization: Department of Electrical and Computer Engineering, University of Washington, Bothell, WA, USA – sequence: 2 givenname: Dara D. surname: Koozekanani fullname: Koozekanani, Dara D. email: dkoozeka@umn.edu organization: Department of Ophthalmology, University of Minnesota, Minneapolis, MN, USA – sequence: 3 givenname: Keshab K. surname: Parhi fullname: Parhi, Keshab K. email: parhi@umn.edu organization: Department of Electrical & Computer Engineering, University of Minnesota, Minneapolis, MN, USA
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/25700436$$D View this record in MEDLINE/PubMed
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References	ref35 ref13 ref34 ref12 ref15 ref36 (ref32) 2011 ref31 ref30 ref11 ref10 ref1 ref17 ref16 ref19 ref18 niemeijer (ref14) 0; 5370 (ref3) 0 ref24 ref23 ref26 ref25 ref20 ref22 abramoff (ref4) 2010; 3 ref21 budai (ref28) 0 ref27 ref29 ref8 gonzalez (ref33) 1992 ref7 ref9 ref6 ref5 karperien (ref2) 2008; 2
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Snippet	This paper presents a novel unsupervised iterative blood vessel segmentation algorithm using fundus images. First, a vessel enhanced image is generated by...
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SubjectTerms	accuracy Algorithms Biomedical imaging Blood vessels computational complexity Databases, Factual Diagnostic Techniques, Ophthalmological fundus image Fundus Oculi Humans Image Processing, Computer-Assisted - methods Image segmentation iterative algorithm Iterative algorithms morphological reconstruction Observers Pathology Retina Retinal Vessels - anatomy & histology ROC Curve stopping criterion Vessel segmentation
Title	Iterative Vessel Segmentation of Fundus Images
URI	https://ieeexplore.ieee.org/document/7042289 https://www.ncbi.nlm.nih.gov/pubmed/25700436 https://www.proquest.com/docview/1690207997
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