Advertisement

Soft Computing

, Volume 22, Issue 5, pp 1501–1509 | Cite as

Automatic vessel segmentation on fundus images using vessel filtering and fuzzy entropy

  • Huiqian Wang
  • Yuhao Jiang
  • Xiaoming Jiang
  • Jun Wu
  • Xiaomin Yang
Focus
  • 114 Downloads

Abstract

Vessel segmentation is a critical and challenging task for fundus image processing, which is precursor and essential first step to further vessel measurement and diagnosis. This paper proposes a novel hybrid automatic vessel segmentation method for the delineation of vessels on fundus images. The method consists of two main steps including Hessian-based vessel filtering and vessel segmentation. In vessel filtering, multi-scale linear filtering based on Hessian matrix is adapted to enhance vessels in the image. After vessel filtering, a novel two-dimensional histogram of filtering image is generated. Then, the thresholds are determined by the fuzzy entropic concepts. We demonstrate the effectiveness of the proposed method on real fundus images from DRIVE database. Quantification analysis is applied through three metrics with respect to manual delineated ground truth from one specialist. Compared to three other methods, the proposed method yields more complete and accurate results.

Keywords

Vessel segmentation Fundus image Hessian matrix Vessel filtering Fuzzy entropic thresholding 

Notes

Acknowledgements

This work is supported by the National Science Foundation of China (Grant Nos. 61471075, 61671091), National Key Technology Research and Development Program of the Ministry of Science and Technology of China (2014BAI11B10), Chongqing Integrated Demonstration Project (CSTC2013jcsf10029), Wenfeng Innovation Foundation of CQUPT, University Innovation Team Construction Plan Funding Project of Chongqing (Smart Medical System and Key Techniques, CXTDG201602009), Chongqing Key Laboratory Improvement Plan (Chongqing Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, cstc2014pt-sy40001), Chongqing Research Program of Basic Research and Frontier Technology (cstc2017jcyjBX0057, cstc2017jcyjAX0328), Science and Technology research project of Chongqing Education Commission (KJ1704073), the Scientific Research Foundation of CQUPT(A2016-73), Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD) Fund, and Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology(CICAEET) Fund.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Abutaleb AS (1989) Automatic thresholding of gray-level pictures using two-dimensional entropy. Comput Vis Gr Image Process 47:22–32. doi: 10.1016/0734-189X(89)90051-0 CrossRefGoogle Scholar
  2. Al-Diri B, Hunter A, Steel D (2009) An active contour model for segmenting and measuring retinal vessels. IEEE Trans Med Imaging 28:1488–1497CrossRefGoogle Scholar
  3. Chaudhuri S, Chatterjee S, Katz N, Nelson M, Goldbaum M (1989) Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans Med Imaging 8:263–269. doi: 10.1109/42.34715 CrossRefGoogle Scholar
  4. Chen B, Shu H, Coatrieux G, Chen G, Sun X, Coatrieux JL (2015) Color image analysis by quaternion-type moments. J Math Imaging Vis 51:625–628MathSciNetCrossRefMATHGoogle Scholar
  5. Cheng HD, Chen YH, Jiang XH (2000) Thresholding using two-dimensional histogram and fuzzy entropy principle. IEEE Trans Image Process 9:732–735CrossRefGoogle Scholar
  6. Dufour A et al (2013) Filtering and segmentation of 3D angiographic data: advances based on mathematical morphology. Med Image Anal 17:147–164CrossRefGoogle Scholar
  7. Espona L, Carreira MJ, Ortega M, Penedo MG (2007) A snake for retinal vessel segmentation. Lect Notes Comput Sci 4478:178–185CrossRefGoogle Scholar
  8. Frangi AF, Niessen WJ, Vincken KL, Viergever MA (1998) Multiscale vessel enhancement filtering. In: Medical image computing and computer-assisted interventation—MICCAI’98. Springer, Berlin, pp 130–137Google Scholar
  9. Frangi AF, Niessen WJ, Nederkoorn PJ, Bakker J, Mali WPTM, Viergever MA (2001) Quantitative analysis of vascular morphology from 3D MR angiograms: in vitro and in vivo results. Magn Reson Med 45:311–322CrossRefGoogle Scholar
  10. Gang L, Chutatape O, Krishnan SM (2002) Detection and measurement of retinal vessels in fundus images using amplitude modified second-order Gaussian filter. IEEE Trans Biomed Eng 49:168–172. doi: 10.1109/10.979356 CrossRefGoogle Scholar
  11. Kirbas C, Quek F (2002) A review of vessel extraction techniques and algorithms. ACM Comput Surv 36:81–121CrossRefGoogle Scholar
  12. Li J, Li X, Yang B, Sun X (2015) Segmentation-based image copy-move forgery detection scheme. IEEE Trans Inf Forensics Secur 10:507–518. doi: 10.1109/TIFS.2014.2381872 CrossRefGoogle Scholar
  13. Lindeberg T (1994) Scale-space theory in computer vision. Springer, New YorkCrossRefMATHGoogle Scholar
  14. Mendonça AM, Campilho A (2006) Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Trans Med Imaging 25:1200–1213CrossRefGoogle Scholar
  15. Shanmugam V, Wahida Banu RSD (2013) Retinal blood vessel segmentation using an extreme learning machine approach. In: 2013 point-of-care healthcare technologies, pp 318–321. doi: 10.1109/PHT.2013.6461349
  16. Su R, Sun C, Pham TD (2012) Junction detection for linear structures based on Hessian, correlation and shape information. Pattern Recogn 45:3695–3706. doi: 10.1016/j.patcog.2012.04.013 CrossRefGoogle Scholar
  17. Su R, Sun C, Zhang C, Pham TD (2014) A new method for linear feature and junction enhancement in 2D images based on morphological operation, oriented anisotropic Gaussian function and Hessian information. Pattern Recogn 47:3193–3208CrossRefGoogle Scholar
  18. Tian Q, Chen S (2017) Cross-heterogeneous-database age estimation through correlation representation learning. Neurocomputing 238:286–295. doi: 10.1016/j.neucom.2017.01.064 CrossRefGoogle Scholar
  19. Vlachos M, Dermatas E (2010) Multi-scale retinal vessel segmentation using line tracking. Comput Med Imaging Gr 34:213–227CrossRefGoogle Scholar
  20. Voorn M, Exner U, Rath A (2013) Multiscale Hessian fracture filtering for the enhancement and segmentation of narrow fractures in 3D image data. Comput Geosci 57:44–53CrossRefGoogle Scholar
  21. Wang J, Li T, Shi Y-Q, Lian S, Ye J (2016) Forensics feature analysis in quaternion wavelet domain for distinguishing photographic images and computer graphics. Multimed Tools Appl. doi: 10.1007/s11042-016-4153-0 Google Scholar
  22. Wen X, Shao L, Xue Y, Fang W (2015) A rapid learning algorithm for vehicle classification. Inf Sci 295:395–406CrossRefGoogle Scholar
  23. Winder RJ, Morrow PJ, Mcritchie IN, Bailie JR, Hart PM (2009) Algorithms for digital image processing in diabetic retinopathy. Comput Med Image Gr 33:608–622. doi: 10.1016/j.compmedimag.2009.06.003 CrossRefGoogle Scholar
  24. Xia Z, Wang X, Sun X, Wang B (2014) Steganalysis of least significant bit matching using multi-order differences. Secur Commun Netw 7:1283–1291CrossRefGoogle Scholar
  25. Xiaoyi J, Mojon D (2003) Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images. IEEE Trans Pattern Anal Mach Intell 25:131–137. doi: 10.1109/TPAMI.2003.1159954 CrossRefGoogle Scholar
  26. Yang Y, Huang S, Rao N (2012) An automatic hybrid method for retinal blood vessel extraction. Int J Appl Math Comput Sci 18:399–407MATHGoogle Scholar
  27. Yin X, Ng BWH, He J, Zhang Y, Abbott D (2014) Accurate image analysis of the retina using hessian matrix and binarisation of thresholded entropy with application of texture mapping. PLoS ONE 9:e95943. doi: 10.1371/journal.pone.0095943 CrossRefGoogle Scholar
  28. Yuan C, Sun X, Lv R (2016) Fingerprint liveness detection based on multi-scale LPQ and PCA. China Commun 13:60–65. doi: 10.1109/CC.2016.7559076 CrossRefGoogle Scholar
  29. Zana F, Klein JC (2001) Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE Trans Image Process 10:1010–1019CrossRefMATHGoogle Scholar
  30. Zhang L, Li Q, You J, Zhang D (2009) A modified matched filter with double-sided thresholding for screening proliferative diabetic retinopathy. IEEE Trans Inf Technol Biomed 13:528–534. doi: 10.1109/TITB.2008.2007201
  31. Zheng Y, Byeungwoo J, Xu D, Wu QMJ, Hui Z (2015) Image segmentation by generalized hierarchical fuzzy C-means algorithm. J Intell Fuzzy Syst 28:4024–4028Google Scholar
  32. Zhou Z, Jonathan Wu QM, Huang F, Sun X (2017a) Fast and accurate near-duplicate image elimination for visual sensor networks. Int J Distrib Sens Netw. doi: 10.1177/1550147717694172 Google Scholar
  33. Zhou Z, Wang Y, Wu QMJ, Yang CN, Sun X (2017b) Effective and efficient global context verification for image copy detection. IEEE Trans Inf Forensics Secur 12:48–63. doi: 10.1109/TIFS.2016.2601065 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Huiqian Wang
    • 1
  • Yuhao Jiang
    • 1
  • Xiaoming Jiang
    • 1
  • Jun Wu
    • 2
  • Xiaomin Yang
    • 3
  1. 1.Chongqing University of Posts and TelecommunicationsChongqingChina
  2. 2.Kaize CompanyChongqingChina
  3. 3.Sichuan UniversityChengduChina

Personalised recommendations