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, Volume 78, Issue 24, pp 34839–34865 | Cite as

Blood vessel segmentation of retinal image using Clifford matched filter and Clifford convolution

  • Somasis Roy
  • Anirban Mitra
  • Sudipta RoyEmail author
  • Sanjit Kumar Setua
Article
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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.

Keywords

Blood vessel segmentation Clifford algebra Clifford convolution Fundus images Multi-vector RGB color model 
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Notes

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.

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringCalcutta University Technology CampusKolkataIndia
  2. 2.Department of Computer Science & EngineeringAcademy of TechnologyAdisaptagramIndia
  3. 3.Mallinckrodt Institute of RadiologyWashington University in Saint LouisSaint LouisUSA

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