Modified pixel level snake using bottom hat transformation for evolution of retinal vasculature map

Meenu Garg; Sheifali Gupta; Soumya Ranjan Nayak; Janmenjoy Nayak; Danilo Pelusi; Meenu Garg; Sheifali Gupta; Soumya Ranjan Nayak; Janmenjoy Nayak; Danilo Pelusi

doi:10.3934/mbe.2021290

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 5: 5737-5757. doi: 10.3934/mbe.2021290

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Research article

Modified pixel level snake using bottom hat transformation for evolution of retinal vasculature map

1.
Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
2.
Amity School of Engineering and Technology, Amity University Uttar Pradesh, Noida, India
3.
Aditya Institute of Technology and Management, Tekkali, K. Kotturu, Andhra Pradesh, India
4.
Faculty of Communication Sciences, University of Teramo, Italy

Received: 28 March 2021 Accepted: 15 June 2021 Published: 25 June 2021

Small changes in retinal blood vessels may produce different pathological disorders which may further cause blindness. Therefore, accurate extraction of vasculature map of retinal fundus image has become a challenging task for analysis of different pathologies. The present study offers an unsupervised method for extraction of vasculature map from retinal fundus images. This paper presents the methodology for evolution of vessels using Modified Pixel Level Snake (MPLS) algorithm based on Black Top-Hat (BTH) transformation. In the proposed method, initially bimodal masking is used for extraction of the mask of the retinal fundus image. Then adaptive segmentation and global thresholding is applied on masked image to find the initial contour image. Finally, MPLS is used for evolution of contour in all four cardinal directions using external, internal and balloon potential. This proposed work is implemented using MATLAB software. DRIVE and STARE databases are used for checking the performance of the system. In the proposed work, various performance metrics such as sensitivity, specificity and accuracy are evaluated. The average sensitivity of 76.96%, average specificity of 98.34% and average accuracy of 96.30% is achieved for DRIVE database. This technique can also segment vessels of pathological images accurately; reaching the average sensitivity of 70.80%, average specificity of 96.40% and average accuracy of 94.41%. The present study provides a simple and accurate method for the detection of vasculature map for normal fundus images as well as pathological images. It can be helpful for the assessment of various retinal vascular attributes like length, diameter, width, tortuosity and branching angle.
- fundus image,
- retinal vasculature,
- bimodal masking,
- global thresholding,
- pixel level snake
Citation: Meenu Garg, Sheifali Gupta, Soumya Ranjan Nayak, Janmenjoy Nayak, Danilo Pelusi. Modified pixel level snake using bottom hat transformation for evolution of retinal vasculature map[J]. Mathematical Biosciences and Engineering, 2021, 18(5): 5737-5757. doi: 10.3934/mbe.2021290

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Abstract

Small changes in retinal blood vessels may produce different pathological disorders which may further cause blindness. Therefore, accurate extraction of vasculature map of retinal fundus image has become a challenging task for analysis of different pathologies. The present study offers an unsupervised method for extraction of vasculature map from retinal fundus images. This paper presents the methodology for evolution of vessels using Modified Pixel Level Snake (MPLS) algorithm based on Black Top-Hat (BTH) transformation. In the proposed method, initially bimodal masking is used for extraction of the mask of the retinal fundus image. Then adaptive segmentation and global thresholding is applied on masked image to find the initial contour image. Finally, MPLS is used for evolution of contour in all four cardinal directions using external, internal and balloon potential. This proposed work is implemented using MATLAB software. DRIVE and STARE databases are used for checking the performance of the system. In the proposed work, various performance metrics such as sensitivity, specificity and accuracy are evaluated. The average sensitivity of 76.96%, average specificity of 98.34% and average accuracy of 96.30% is achieved for DRIVE database. This technique can also segment vessels of pathological images accurately; reaching the average sensitivity of 70.80%, average specificity of 96.40% and average accuracy of 94.41%. The present study provides a simple and accurate method for the detection of vasculature map for normal fundus images as well as pathological images. It can be helpful for the assessment of various retinal vascular attributes like length, diameter, width, tortuosity and branching angle.

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