New Approach of Fundus Image Segmentation Evaluation Based on Topology Structure_Journal of Biomedical Engineering

Authors：

SHENGHanwei ¹ , DAIPeishan ¹ , LIUZhihang ¹ , WENMiaoyun ¹ , ZHAOYali ¹ ,  FANMin ²

1. Department of Biomedical Engineering, School of Geoscience and Info-Physics, Central South University, Changsha 410083, China;
2. Department of Education and Law, Hunan Women's University, Changsha 410004, China;

Corresponding author：

FANMin, Email: fm11111111@163.com

Keywords：

retinal vessel segmentation; image segmentation evaluation; topology structure

DOI：

10.7507/1001-5515.20150195

Video：

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Abstract Full text Figures/Tables Video References Cited by

In view of the evaluation of fundus image segmentation, a new evaluation method was proposed to make up insufficiency of the traditional evaluation method which only considers the overlap of pixels and neglects topology structure of the retinal vessel. Mathematical morphology and thinning algorithm were used to obtain the retinal vascular topology structure. Then three features of retinal vessel, including mutual information, correlation coefficient and ratio of nodes, were calculated. The features of the thinned images taken as topology structure of blood vessel were used to evaluate retinal image segmentation. The manually-labeled images and their eroded ones of STARE database were used in the experiment. The result showed that these features, including mutual information, correlation coefficient and ratio of nodes, could be used to evaluate the segmentation quality of retinal vessel on fundus image through topology structure, and the algorithm was simple. The method is of significance to the supplement of traditional segmentation evaluation of retinal vessel on fundus image.

Citation： SHENGHanwei, DAIPeishan, LIUZhihang, WENMiaoyun, ZHAOYali, FANMin. New Approach of Fundus Image Segmentation Evaluation Based on Topology Structure. Journal of Biomedical Engineering, 2015, 32(5): 1100-1105. doi: 10.7507/1001-5515.20150195 Copy

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16.	徐维超. 相关系数研究综述[J]. 广东工业大学学报, 2012, 29(3):12-17.
17.	DASHTBOZORG B, MENDONCA A M, CAMPILHO A. An automatic graph-based approach for artery/vein classification in retinal images[J]. IEEE Transactions on Image Processing, 2014, 23(3):1073-1083.
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1. JIANG X Y, MOJON D. Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(1):131-137.
2. LAI N. Clinical Ophthalmology:A systematic approach[J]. Optometry and Vision Science, 2004, 81(5):295.
3. MOOKIANH M R K, ACHARYA U R, CHUA C K, et al. Computer-aided diagnosis of diabetic retinopathy:a review[J]. Computers in Biology and Medicine, 2013, 43(12):2136-2155.
4. FRAZ M M, REMAGNINO P, HOPPE A, et al. Blood vessel segmentation methodologies in retinal images——a survey[J]. Computer Methods and Programs in Biomedicine, 2012, 108(1):407-433.
5. ALINA O, CONSTANTIN V. A quantitative evaluation of the hip prosthesis segmentation quality in X-ray images[C]//Signals, Circuits and Systems-ISSCS 2007. Lasi, Romania:2007:1-4.
6. SOARES J V B, LEANDRO J J G, CESAR R M, et al. Retinal vessel segmentation using the 2-D gabor wavelet and supervised classification[J]. IEEE Transactions on Medical Imaging, 2006, 25(9):1214-1222.
7. UYEN T V, NGUYEN K R. A quantitative measure for retinal blood vessel segmentation evaluation[J]. International Journal of Computer Vision and Signal Processing, 2012, 1(1):1-8.
8. GEGUNDEZ-ARIAS M E, AQUINO A, BRAVO J M, et al. A function for quality evaluation of retinal vessel segmentations[J]. IEEE Transactions on Medical Imaging, 2012, 31(2):231-239.
9. ZHAO Y Q, WANG X H, WANG X F, et al. Retinal vessels segmentation based on level set and region growing[J]. Pattern Recognition, 2014, 47(7):2437-2446.
10. LAM B S Y, HONG Y. A novel vessel segmentation algorithm for pathological retina images based on the divergence of vector fields[J]. IEEE Transactions on Medical Imaging, 2008, 27(2):237-246.
11. WANG Y F, JI G R, LIN P, et al. Retinal vessel segmentation using multiwavelet kernels and multiscale hierarchical decomposition[J]. Pattern Recognition, 2013, 46(8):2117-2133.
12. VERGARA J R, ESTEVEZ P A. A review of feature selection methods based on mutual information[J]. Neural Computing and Applications, 2014, 24(1):175-186.
13. 姜晓彤, 罗立民, 赵正旭. 一种改进的基于互信息和梯度特征的图像配准方法的研究[J]. 仪器仪表学报, 2006, 27(9):1141-1146.
14. STUDHOLME C, HILL D L G, HAWKES D J. An overlap invariant entropy measure of 3D medical image alignment[J]. Pattern Recognition, 1999, 32(1):71-86.
15. DUERSCH M I, LONG D G. Analysis of multistatic pixel correlation in SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(1):362-374.
16. 徐维超. 相关系数研究综述[J]. 广东工业大学学报, 2012, 29(3):12-17.
17. DASHTBOZORG B, MENDONCA A M, CAMPILHO A. An automatic graph-based approach for artery/vein classification in retinal images[J]. IEEE Transactions on Image Processing, 2014, 23(3):1073-1083.
18. 覃森, 戴冠中, 王林. 节点数固定的复杂网络模型初探[J]. 复杂系统与复杂性科学, 2005, 2(2):7-12.
19. 李强, 张钹. 一种基于图像灰度的快速匹配算法[J]. 软件学报, 2006, 17(2):216-222.

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