Enhancing Vessel Segment Extraction in Retinal Fundus Images Using Retinal Image Analysis and Six Sigma Process Capability Index
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 a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/14/3170 |
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| author | Sufian A. Badawi Maen Takruri Isam ElBadawi Imran Ali Chaudhry Nasr Ullah Mahar Ajay Kamath Nileshwar Emad Mosalam |
| author_facet | Sufian A. Badawi Maen Takruri Isam ElBadawi Imran Ali Chaudhry Nasr Ullah Mahar Ajay Kamath Nileshwar Emad Mosalam |
| author_sort | Sufian A. Badawi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T00:52:01Z |
| format | Article |
| id | doaj.art-ad94a13773d84e76b3bca60192aee449 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T00:52:01Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-ad94a13773d84e76b3bca60192aee4492023-11-18T20:21:37ZengMDPI AGMathematics2227-73902023-07-011114317010.3390/math11143170Enhancing Vessel Segment Extraction in Retinal Fundus Images Using Retinal Image Analysis and Six Sigma Process Capability IndexSufian A. Badawi0Maen Takruri1Isam ElBadawi2Imran Ali Chaudhry3Nasr Ullah Mahar4Ajay Kamath Nileshwar5Emad Mosalam6Center of Information, Communication and Networking Education and Innovation (ICONET), American University of Ras Al Khaimah, Ras Al Khaimah 72603, United Arab EmiratesCenter of Information, Communication and Networking Education and Innovation (ICONET), American University of Ras Al Khaimah, Ras Al Khaimah 72603, United Arab EmiratesIndustrial Engineering Department, College of Engineering, University of Ha’il, Ha’il 81481, Saudi ArabiaIndustrial Engineering Department, College of Engineering, University of Ha’il, Ha’il 81481, Saudi ArabiaComputer Science Department, Bahauddin Zakariya University, Multan 60800, PakistanDepartment of Ophthalmology, RAK Medical and Health Sciences University, Ras Al Khaimah 11172, United Arab EmiratesDr. Emad Mussalam Eye Clinic, Ras Al Khaimah P.O. Box 5450, United Arab EmiratesRetinal 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.https://www.mdpi.com/2227-7390/11/14/3170retinal imagesretinal blood vesselsskeletonizationtortuosityinflection count metricprocess capability index |
| spellingShingle | Sufian A. Badawi Maen Takruri Isam ElBadawi Imran Ali Chaudhry Nasr Ullah Mahar Ajay Kamath Nileshwar Emad Mosalam Enhancing Vessel Segment Extraction in Retinal Fundus Images Using Retinal Image Analysis and Six Sigma Process Capability Index Mathematics retinal images retinal blood vessels skeletonization tortuosity inflection count metric process capability index |
| title | Enhancing Vessel Segment Extraction in Retinal Fundus Images Using Retinal Image Analysis and Six Sigma Process Capability Index |
| title_full | Enhancing Vessel Segment Extraction in Retinal Fundus Images Using Retinal Image Analysis and Six Sigma Process Capability Index |
| title_fullStr | Enhancing Vessel Segment Extraction in Retinal Fundus Images Using Retinal Image Analysis and Six Sigma Process Capability Index |
| title_full_unstemmed | Enhancing Vessel Segment Extraction in Retinal Fundus Images Using Retinal Image Analysis and Six Sigma Process Capability Index |
| title_short | Enhancing Vessel Segment Extraction in Retinal Fundus Images Using Retinal Image Analysis and Six Sigma Process Capability Index |
| title_sort | enhancing vessel segment extraction in retinal fundus images using retinal image analysis and six sigma process capability index |
| topic | retinal images retinal blood vessels skeletonization tortuosity inflection count metric process capability index |
| url | https://www.mdpi.com/2227-7390/11/14/3170 |
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