Retinal Vessel Segmentation: An Efficient Graph Cut Approach with Retinex and Local Phase

PLOS ONE, Dec 2019

Our application concerns the automated detection of vessels in retinal images to improve understanding of the disease mechanism, diagnosis and treatment of retinal and a number of systemic diseases. We propose a new framework for segmenting retinal vasculatures with much improved accuracy and efficiency. The proposed framework consists of three technical components: Retinex-based image inhomogeneity correction, local phase-based vessel enhancement and graph cut-based active contour segmentation. These procedures are applied in the following order. Underpinned by the Retinex theory, the inhomogeneity correction step aims to address challenges presented by the image intensity inhomogeneities, and the relatively low contrast of thin vessels compared to the background. The local phase enhancement technique is employed to enhance vessels for its superiority in preserving the vessel edges. The graph cut-based active contour method is used for its efficiency and effectiveness in segmenting the vessels from the enhanced images using the local phase filter. We have demonstrated its performance by applying it to four public retinal image datasets (3 datasets of color fundus photography and 1 of fluorescein angiography). Statistical analysis demonstrates that each component of the framework can provide the level of performance expected. The proposed framework is compared with widely used unsupervised and supervised methods, showing that the overall framework outperforms its competitors. For example, the achieved sensitivity (0:744), specificity (0:978) and accuracy (0:953) for the DRIVE dataset are very close to those of the manual annotations obtained by the second observer.

A PDF file should load here. If you do not see its contents the file may be temporarily unavailable at the journal website or you do not have a PDF plug-in installed and enabled in your browser.

Alternatively, you can download the file locally and open with any standalone PDF reader:

https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0122332&type=printable

Retinal Vessel Segmentation: An Efficient Graph Cut Approach with Retinex and Local Phase

April Retinal Vessel Segmentation: An Efficient Graph Cut Approach with Retinex and Local Phase Yitian Zhao 0 1 2 3 Yonghuai Liu 0 1 2 3 Xiangqian Wu 0 1 2 3 Simon P. Harding 0 1 2 3 Yalin Zheng 0 1 2 3 0 1 Department of Eye and Vision Science, University of Liverpool , Liverpool , United Kingdom , 2 Department of Computer Science, Aberystwyth University , Aberystwyth , United Kingdom , 3 School of Computer Science and Technology, Harbin Institute of Technology , Harbin, China, 4 St Paul's Eye Unit , Royal Liverpool University Hospital , Liverpool , United Kingdom 1 Funding: This work was supported by the Wellcome Trust (grant number 092668/Z/10/Z). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript 2 Academic Editor: Stefan Strack, University of Iowa, UNITED STATES 3 Current address: School of Mechatronical Engineering, Beijing Institute of Technology , Beijing , China Our application concerns the automated detection of vessels in retinal images to improve understanding of the disease mechanism, diagnosis and treatment of retinal and a number of systemic diseases. We propose a new framework for segmenting retinal vasculatures with much improved accuracy and efficiency. The proposed framework consists of three technical components: Retinex-based image inhomogeneity correction, local phase-based vessel enhancement and graph cut-based active contour segmentation. These procedures are applied in the following order. Underpinned by the Retinex theory, the inhomogeneity correction step aims to address challenges presented by the image intensity inhomogeneities, and the relatively low contrast of thin vessels compared to the background. The local phase enhancement technique is employed to enhance vessels for its superiority in preserving the vessel edges. The graph cut-based active contour method is used for its efficiency and effectiveness in segmenting the vessels from the enhanced images using the local phase filter. We have demonstrated its performance by applying it to four public retinal image datasets (3 datasets of color fundus photography and 1 of fluorescein angiography). Statistical analysis demonstrates that each component of the framework can provide the level of performance expected. The proposed framework is compared with widely used unsupervised and supervised methods, showing that the overall framework outperforms its competitors. For example, the achieved sensitivity (0:744), specificity (0:978) and accuracy (0:953) for the DRIVE dataset are very close to those of the manual annotations obtained by the second observer. - Competing Interests: The authors have declared that no competing interests exist. vasculature can result from several diseases [1]. Vascular abnormalities can be seen in various retinal diseases. Study of the retinal circulation is of great importance in the management of retinal diseases, but also provides unique opportunity to study the microvascular damage to the brain in cerebral malaria [2]. Structural changes in the retinal vasculature may also indicate hypertension, stroke, heart disease and nephropathy [3]. The retina is visible to examination and accessible to high-resolution, non-invasive imaging. This provided a unique window that allows direct visualization and analysis of the inner retinal vascular circulation for studying various related conditions. Automated analysis of the retinal vasculature becomes an active research area in the field of medical imaging for its diagnostic and prognostic significance. Our application concerns the automated detection of retinal blood vessels in diagnostic retinal images such as color fundus images and fluorescein angiography images. The automated detection of blood vessels is a prerequisite in the development of automated system for the analysis of vessels. Recent years have witnessed the rapid development of methods for retinal vessel segmentation, as evidenced by extensive reviews [4, 5]. For the purpose of this paper this list is intended only to provide readers with some insight into this problem domain, and is by no means exhaustive. Most existing methods are automated techniques without interaction from the user during the segmentation. However, we noted that interactive segmentation techniques, such as Live Vessel [6], were proposed for improving the segmentation performance. Broadly speaking, all the established automated segmentation techniques may be categorized as either supervised segmentation [713] or unsupervised segmentation [1423] with respect to the overall system design and architecture. Supervised segmentation requires hand-labeled gold standard images for training, and each pixel is represented by a feature vector which is obtained from local or global information of the image. The prerequisite for this approach is that a set of features having the necessary discriminative ability have to be extracted for training and classificati (...truncated)


This is a preview of a remote PDF: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0122332&type=printable

Yitian Zhao, Yonghuai Liu, Xiangqian Wu, Simon P. Harding, Yalin Zheng. Retinal Vessel Segmentation: An Efficient Graph Cut Approach with Retinex and Local Phase, PLOS ONE, 2015, Volume 10, Issue 4, DOI: 10.1371/journal.pone.0122332