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Enhancing Vessel Segment Extraction in Retinal Fundus Images Using Retinal Image Analysis and Six Sigma Process Capability Index

Author

Listed:
  • Sufian A. Badawi

    (Center of Information, Communication and Networking Education and Innovation (ICONET), American University of Ras Al Khaimah, Ras Al Khaimah 72603, United Arab Emirates)

  • Maen Takruri

    (Center of Information, Communication and Networking Education and Innovation (ICONET), American University of Ras Al Khaimah, Ras Al Khaimah 72603, United Arab Emirates)

  • Isam ElBadawi

    (Industrial Engineering Department, College of Engineering, University of Ha’il, Ha’il 81481, Saudi Arabia)

  • Imran Ali Chaudhry

    (Industrial Engineering Department, College of Engineering, University of Ha’il, Ha’il 81481, Saudi Arabia)

  • Nasr Ullah Mahar

    (Computer Science Department, Bahauddin Zakariya University, Multan 60800, Pakistan)

  • Ajay Kamath Nileshwar

    (Department of Ophthalmology, RAK Medical and Health Sciences University, Ras Al Khaimah 11172, United Arab Emirates
    Saqr Hospital, Ministry of Health and Prevention, Ras Al Khaimah P.O. Box 5450, United Arab Emirates)

  • Emad Mosalam

    (Dr. Emad Mussalam Eye Clinic, Ras Al Khaimah P.O. Box 5450, United Arab Emirates)

Abstract

Retinal vessel segmentation, skeletonization, and the generation of vessel segments are considered significant steps in any automated system for measuring the vessel biomarkers of several disease diagnoses. Most of the current tortuosity quantification methods rely on precise vascular segmentation and skeletonization of the retinal vessels. Additionally, the existence of a reference dataset for accurate vessel segment images is an essential need for implementing deep learning solutions and an automated system for measuring the vessel biomarkers of several disease diagnoses, especially for optimized quantification of vessel tortuosity or accurate measurement of AV-nicking. This study aimed to present an improved method for skeletonizing and extracting the retinal vessel segments from the 504 images in the AV classification dataset. The study utilized the Six Sigma process capability index, sigma level, and yield to measure the vessels’ tortuosity calculation improvement before and after optimizing the extracted vessels. As a result, the study showed that the sigma level for the vessel segment optimization improved from 2.7 to 4.39, the confirming yield improved from 88 percent to 99.77 percent, and the optimized vessel segments of the AV classification dataset retinal images are available in monochrome and colored formats.

Suggested Citation

  • Sufian A. Badawi & Maen Takruri & Isam ElBadawi & Imran Ali Chaudhry & Nasr Ullah Mahar & Ajay Kamath Nileshwar & Emad Mosalam, 2023. "Enhancing Vessel Segment Extraction in Retinal Fundus Images Using Retinal Image Analysis and Six Sigma Process Capability Index," Mathematics, MDPI, vol. 11(14), pages 1-32, July.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:14:p:3170-:d:1197549
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    References listed on IDEAS

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    1. Peishan Dai & Hanyuan Luo & Hanwei Sheng & Yali Zhao & Ling Li & Jing Wu & Yuqian Zhao & Kenji Suzuki, 2015. "A New Approach to Segment Both Main and Peripheral Retinal Vessels Based on Gray-Voting and Gaussian Mixture Model," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-22, June.
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