Computer aided diagnoses for detecting the severity of Keratoconus
Problem: Corneal topography instruments have limited parameter constraints for calculating precise defect ratios on the basis of the cone base area of the anterior axial curvature map for patients with Keratoconus (KC). Aim: The aim of this study is to use thresholding-based segmentation and morphol...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Walter de Gruyter
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/114298/1/114298.pdf |
| _version_ | 1824452382529945600 |
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| author | Abdullah, Osamah Qays Boughariou, Aicha Al-Azawi, Fadia W. Al-Araji, Ahmed Mohammed Khadum Abdulamer Mehdy, Mehdy Mwaffeq |
| author_facet | Abdullah, Osamah Qays Boughariou, Aicha Al-Azawi, Fadia W. Al-Araji, Ahmed Mohammed Khadum Abdulamer Mehdy, Mehdy Mwaffeq |
| author_sort | Abdullah, Osamah Qays |
| collection | UPM |
| description | Problem: Corneal topography instruments have limited parameter constraints for calculating precise defect ratios on the basis of the cone base area of the anterior axial curvature map for patients with Keratoconus (KC). Aim: The aim of this study is to use thresholding-based segmentation and morphological techniques to calculate the pathological ratio of the keratoconic cornea through cone base area extraction for the detection of KC severity and tracking of disease development. Methods: Data were collected from February 2022 to March 2023, comprising 97 cases from private clinics in southern Iraq. Disease severity was categorized into three stages, namely, mild, moderate, and severe, according to the topographic KC classification by a senior ophthalmologist. The Galilei system was used in obtaining the corneal topography images. The study proposed an image analysis method for corneal topography images using MATLAB R2020a. The method had four main steps: preprocessing, image segmentation, morphological processing, and pathological ratio calculation. Moreover, pathological ratio was compared with the KC severity through statistical analysis. A P-value less than 0.05 indicated statistically significant results. Results: The majority of the cases in the mild category had a pathological ratio of ≤20%, and the moderate category had a higher prevalence ranging from 21 to 40%. The severe category had the highest distribution (<40%). A P-value of <0.001 indicated significant and clear link between KC stages and pathologic ratio. Conclusion: The algorithm used for extracting the cone base area of the keratoconic cornea at different stages was validated by an ophthalmic specialist to ensure that the cone base area was appropriately extracted. The findings may help ophthalmologists to make informed decisions for patients with severe KC and assessments based on the percentage of corneal defects. |
| first_indexed | 2025-02-19T02:49:38Z |
| format | Article |
| id | upm.eprints-114298 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2025-02-19T02:49:38Z |
| publishDate | 2024 |
| publisher | Walter de Gruyter |
| record_format | dspace |
| spelling | upm.eprints-1142982025-01-13T08:21:41Z http://psasir.upm.edu.my/id/eprint/114298/ Computer aided diagnoses for detecting the severity of Keratoconus Abdullah, Osamah Qays Boughariou, Aicha Al-Azawi, Fadia W. Al-Araji, Ahmed Mohammed Khadum Abdulamer Mehdy, Mehdy Mwaffeq Problem: Corneal topography instruments have limited parameter constraints for calculating precise defect ratios on the basis of the cone base area of the anterior axial curvature map for patients with Keratoconus (KC). Aim: The aim of this study is to use thresholding-based segmentation and morphological techniques to calculate the pathological ratio of the keratoconic cornea through cone base area extraction for the detection of KC severity and tracking of disease development. Methods: Data were collected from February 2022 to March 2023, comprising 97 cases from private clinics in southern Iraq. Disease severity was categorized into three stages, namely, mild, moderate, and severe, according to the topographic KC classification by a senior ophthalmologist. The Galilei system was used in obtaining the corneal topography images. The study proposed an image analysis method for corneal topography images using MATLAB R2020a. The method had four main steps: preprocessing, image segmentation, morphological processing, and pathological ratio calculation. Moreover, pathological ratio was compared with the KC severity through statistical analysis. A P-value less than 0.05 indicated statistically significant results. Results: The majority of the cases in the mild category had a pathological ratio of ≤20%, and the moderate category had a higher prevalence ranging from 21 to 40%. The severe category had the highest distribution (<40%). A P-value of <0.001 indicated significant and clear link between KC stages and pathologic ratio. Conclusion: The algorithm used for extracting the cone base area of the keratoconic cornea at different stages was validated by an ophthalmic specialist to ensure that the cone base area was appropriately extracted. The findings may help ophthalmologists to make informed decisions for patients with severe KC and assessments based on the percentage of corneal defects. Walter de Gruyter 2024 Article PeerReviewed text en cc_by_4 http://psasir.upm.edu.my/id/eprint/114298/1/114298.pdf Abdullah, Osamah Qays and Boughariou, Aicha and Al-Azawi, Fadia W. and Al-Araji, Ahmed Mohammed Khadum Abdulamer and Mehdy, Mehdy Mwaffeq (2024) Computer aided diagnoses for detecting the severity of Keratoconus. Journal of Intelligent Systems, 33 (1). art. no. 20240287. pp. 1-13. ISSN 2191-026X; eISSN: 2191-026X https://www.degruyter.com/document/doi/10.1515/jisys-2024-0287/html 10.1515/jisys-2024-0287 |
| spellingShingle | Abdullah, Osamah Qays Boughariou, Aicha Al-Azawi, Fadia W. Al-Araji, Ahmed Mohammed Khadum Abdulamer Mehdy, Mehdy Mwaffeq Computer aided diagnoses for detecting the severity of Keratoconus |
| title | Computer aided diagnoses for detecting the severity of Keratoconus |
| title_full | Computer aided diagnoses for detecting the severity of Keratoconus |
| title_fullStr | Computer aided diagnoses for detecting the severity of Keratoconus |
| title_full_unstemmed | Computer aided diagnoses for detecting the severity of Keratoconus |
| title_short | Computer aided diagnoses for detecting the severity of Keratoconus |
| title_sort | computer aided diagnoses for detecting the severity of keratoconus |
| url | http://psasir.upm.edu.my/id/eprint/114298/1/114298.pdf |
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