Abstract
The appearance and structure of blood vessels in retinal fundus image is a fundamental part of diagnosing different issues related with such as diabetes and hypertension. The proposed blood vessel segmentation in fundus image using Clifford Algebra approach is divided into three steps. Image vectorization as a first step helps to convert the image space into Clifford space. Next step introduces Clifford matched filter as a proposed mask which works for retinal blood vessel extraction. The third and final step of this method is Clifford convolution operation with the help of Clifford convolution. This mask generates edge points along the boundaries of the blood vessels. The edge points are represented as a Grade-0 vector or scalar unit. Discrete edge points along the boundary of blood vessels are the edge pixels instead of continuous edges. The output of this method differs in the representation of vessel tree compare to other existing methods. The output image can be defined as the edge point set. This method achieves blood vessel segmentation accuracy of 94.88% and 92.95% on two publicly available datasets STARE and DRIVE respectively in less than 0.5 s per image. The proposed matched filter and the segmentation technique opens many windows of reliable and faster processing for further image processing steps on retinal fundus images.
Similar content being viewed by others
References
Armande N, Montesinos P, Monga O (1997) Thin Nets Extraction using a Multi-Scale Approach. SCALE-SPACE ‟97: Proceedings of the First International Conference on Scale-Space Theory in Computer Vision, Springer-Verlag, pp. 361–364
Azzopardi G et al (2015) Trainable COSFIRE filters for vessel delineation with application to retinal images. Med Image Anal 19(1):46–57
Batard T, Berthier M (2010) Clifford algebra bundles to multidimensional image segmentation. AACA 20(3–4):489–516
Batard T, Saint-Jean C, Berthier M (2009) A metric approach to nD images edge detection with Clifford algebras. Journal of Mathematical Imaging and Vision 33(3):296–312
Bhalerao A, Wilson R (2001) Estimating Local and Global Image Structure using a Gaussian Intensity Model. Medical Image Understanding and Analysis
Carré P, Denis P, Fernandez-Maloigne C (2014) Spatial color image processing using Clifford algebras: application to color active contour. SIViP 8(7):1357–1372
Chanwimaluang T, Fan G (2003) An Efficient Blood Vessel Detection Algorithm for Retinal Images using Local Entropy Thresholding. In: Proc. of the IEEE International Symposium on Circuits and Systems, Bangkok, vol.5, pp.21–24
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(3):263–269
Dash J, Bhoi N (2016) A method for blood vessel segmentation in retinal images using morphological reconstruction. Computer, Electrical & Communication Engineering (ICCECE), 2016 International Conference on. IEEE
Denis P, Carré P (2007) Colour gradient using geometric algebra." Signal Processing Conference, 2007 15th European. IEEE
Dian Tunjung N, Arifin AZ, Soelaiman R. Medical image segmentation using generalized gradient vector flow and Clifford geometric algebra
Dorst L, Mann S (2002) Geometric Algebra: A Computational Framework for Geometrical Applications(I). IEEE Comput Graph Appl
Ebling J, Scheuermann G (2003) Clifford Convolution and Pattern Matching On Vector Fields. Proceedings of IEEE Visualization:193–200, 2003
Ell TA (2007) Multi-vector color-image filters. 2007 IEEE International Conference on Image Processing. Vol. 5. IEEE
Fang B, Hsu W, Lee M (2003) Reconstruction of Vascular Structures in Retinal Images. Proc. ICIP‟03, pp. 157–160
Fathi A, Naghsh-Nilchi AR (2012) Automatic wavelet-based retinal blood vessels segmentation and vessel diameter estimation. Biomedical Signal Pro-cessing and Control 8(1):71–80
Frame A, McCree M, Olson J, McHardy K, Sharp P, Forrester JV (1997) Structural Analysis of Retinal Vessels. Proceedings of the Sixth International Conference on Image Processing and its Applications, IEEE, vol.2, pp. 824–827
Franchini S, et al (2012) Clifford Algebra Based Edge Detector for Color Images. Complex, Intelligent and Software Intensive Systems (CISIS), 2012 Sixth International Conference on. IEEE
Fraz MM, Remagnino P, Hoppe A, Uyyanonvara B, Rudnicka A, Owen C, Barman S (2012) Blood vessel segmentation methodologies in retinal images – A survey. Comput Methods Prog Biomed 108:407–433
Gonzalez RC, Woods RE (2001) Digital Image Processing, 2nd edition
Grass SHM, Rasche V, O S, Haehnel S, Sartor K (2002) An X-Ray-Based Method for the Determination of the Contrast Agent Propagation in 3-D Vessel Structures. IEEE Trans Med Imaging 21:251–262
Hart WE, Goldbaum M, Cote B, Kube P, Nelson MR (1997) Automated measurement of retinal vascular tortuosity. In: Proceedings of the AMIA Fall Conference
Hoover A, Kouznetsova V, Goldbaum M (2000) Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Transactions onMedical Imaging 19(3):203–210
J¨ahne B (2002) Digitale Bildverarbeitung. Springer Verlag, Berlin
Jiang X, 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(1):131–137
(2009) Learning Geometric Algebra with CLUCalc. In: Geometric Algebra with Applications in Engineering. Geometry and Computing, vol 4. Springer, Berlin, Heidelberg
Li H, Hsu W, Lee ML, Wang H (2005) Automatic Grading of Retinal Vessel Calibre. IEEE Trans Biomed Eng 52:1352–1355
Maharjan A (2016) Blood Vessel Segmentation from Retinal Images. Comput Therm Sci
Maitra IK, Nag S, Bandyopadhyay SK (2011) Automated Digital Mammogram Segmentation for Detection of Abnormal Masses Using Binary Homogeneity Enhancement Algorithm. IJCSE, ISSN No. 0976–5166 2(3):415–427
Mann S, Dorst L, Bouma T (2001) The Making of GABLE: A Geometric Algebra Learning Environment in Matlab. In: Corrochano EB, Sobczyk G (eds) Geometric Algebra with Applications in Science and Engineering. Birkhäuser, Boston
Mishra B, Wilson P, Al-Hashimi BM (2008) Advancement in color image processing using Geometric Algebra. Signal Processing Conference, 2008 16th European. IEEE
Mishra B, Wilson P, Wilcock R (2015) A geometric algebra co-processor for color edge detection. Electronics 4(1):94–117
Mitra A, Roy S, Roy S, Setua SK (2018) Enhancement and Restoration of non-uniform illuminated Fundus Image of Retina obtained through thin layer of Cataract. Comput Methods Prog Biomed, ELSEVIER 156:169–178
Mitra A, Roy S, Setua SK (2014) Morphologically contour extraction of decisive objects from image. Automation, Control, Energy and Systems (ACES), 2014 First International Conference on. IEEE
Mittal M, Verma A, Kaur B, Sharma M, Goyal LM, Kaur I, Roy S, Kim T (2019) An Efficient Edge Detection Approach to Provide Better Edge Connectivity for Image Analysis. IEEE ACCESS 7(1):33240–33255
Mudassar AA, Butt S (2013) Extraction of blood vessels in retinal images using four different techniques. Journal of Medical Engineering 2013
Nguyen UT, Bhuiyan A, Park LA, Ramamohanarao K (2013) An effective retinal blood vessel segmentation method using multi-scale line detection. Pattern Recogn 46:703–715
Pizer SM et al (1987) Adaptive histogram equalization and its variations. Computer Vision, Graphics, and Image Processing 39(3):355–368
Rani P, Priyadarshini N, Rajkumar ER, Rajamani K (2016) Retinal vessel segmentation under pathological conditions using supervised machine learning. In 2016 International Conference on, Systems in Medicine and Biology (ICSMB), pp. 62–66
Reich, Wieland, and Gerik Scheuermann. (2010) Analyzing Real Vector Fields with Clifford Convolution and Clifford-Fourier Transform. pp. 121–133
Ricci E, Perfetti R (2007) Retinal Blood Vessel Segmentation Using Line Operators and Support Vector Classification. IEEE Trans Med Imaging 26(2007):1357–1365
Rivera-Rovelo J, Bayro-Corrochano E (2006) Medical image segmentation using a self-organizing neural network and Clifford geometric algebra. The 2006 IEEE International Joint Conference on Neural Network Proceedings. IEEE
Roy S, Mitra A, Setua SK (2014) Color Image Representation Using Multivector. Intelligent Systems, Modelling and Simulation (ISMS), 2014 5th International Conference on. IEEE
Roychowdhury S, Koozekanani DD, Parhi KK (2015) Blood vessel segmentation of fundus images by major vessel extraction and subimage classification. IEEE Journal of Biomedical and Health Informatics 19(3):1118–1128
Schlemmer M, et al (2006) Clifford pattern matching for color image edge detection. Visualization of Large and Unstructured Data Sets, GI-Edition Lecture Notes in Informatics (LNI) 4, pp. 47–58
Shah SAA, Tang TB, Faye I, Laude A (2017) Blood vessel segmentation in color fundus images based on regional and Hessian features. Graefes Arch Clin Exp Ophthalmol:1–9
Soares JV, Leandro JJ, Cesar RM, Jelinek HF, Cree MJ (2006) Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Transaction of Medical Imaging 25:1214–1222
Staal J, Abr’amoff MD, Niemeijer M, Viergever MA, Van Ginneken B (2004) Ridge-based vessel segmentation in color images of the retina. IEEE Trans Med Imaging 23(4):501–509
Sun K, Chen Z, Jiang S, Wang Y (2011) Morphological multiscale enhance-ment, fuzzy filter and watershed for vascular tree extraction in angiogram. J Med Syst 35(5):811–824
Wang L, Bhalerao A, Wilson R (2007) Analysis of Retinal Vasculature using a Multiresolution Hermite Model. IEEE Trans Med Imaging 26:137–152
Xu L, Luo S (2010) A novel method for blood vessel detection from retinal images. Biomed Eng Online 9(1):14
Yavuz Z, Köse C (2017) Blood Vessel Extraction in Color Retinal Fundus Images with Enhancement Filtering and Unsupervised Classification. Journal of Healthcare Engineering 2017
Yin X-X, Hadjiloucas S, Zhang Y (2017) Outlook for Clifford Algebra Based Feature and Deep Learning AI Architectures. Pattern Classification of Medical Images: Computer Aided Diagnosis. Springer, Cham, pp. 165–177
Zana F, Klein J (2001) Segmentation of Vessel-Like Patterns using Mathematical Morphology and Curvature Evaluation. IEEE Trans Image Process:1010–1019
Zhang Y, Hsu Mong W, Lee L (2007) Segmentation of Retinal Vessels Using Nonlinear Projections. IEEE International Conference on Image Processing 5:541–544
Zhang B, Zhang L, Karray F (2010) Retinal vessel extraction by matched filter with first-order derivative of Gaussian. Comput Biol Med 40:438–445
Zhao YQ et al (2014) Retinal vessels segmentation based on level set and region growing. Pattern Recogn 47(7):2437–2446
Acknowledgements
The authors extend sincere thanks to the Department of Computer Science and Engineering, University of Calcutta West Bengal, India and Academy Of Technology, Hooghly, West Bengal, India for using the infrastructure facilities for developing the technique.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Roy, S., Mitra, A., Roy, S. et al. Blood vessel segmentation of retinal image using Clifford matched filter and Clifford convolution. Multimed Tools Appl 78, 34839–34865 (2019). https://doi.org/10.1007/s11042-019-08111-0
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-019-08111-0