Pupil Detection for Automatic Diagnosis of Eye Diseases Using Optimized Color Mapping
Introduction: Pupil and iris disorders form an important category of eye diseases. Accurate segmentation of the pupil is the first and most important step in the automatic diagnosis of diseases related to the pupil and iris. Most of the existing methods do not have enough accuracy and are sensitive...
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| Format: | Article |
| Language: | fas |
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Kerman University of Medical Sciences
2023-06-01
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| Series: | مجله انفورماتیک سلامت و زیست پزشکی |
| Subjects: | |
| Online Access: | http://jhbmi.ir/article-1-752-en.pdf |
| _version_ | 1797672523731566592 |
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| author | Rasul Kheyrolahi Fatemeh Hoorali Mohammad Hossein Sedaaghi Hossein Ebrahimnezhad |
| author_facet | Rasul Kheyrolahi Fatemeh Hoorali Mohammad Hossein Sedaaghi Hossein Ebrahimnezhad |
| author_sort | Rasul Kheyrolahi |
| collection | DOAJ |
| description | Introduction: Pupil and iris disorders form an important category of eye diseases. Accurate segmentation of the pupil is the first and most important step in the automatic diagnosis of diseases related to the pupil and iris. Most of the existing methods do not have enough accuracy and are sensitive to the effects of noise and specular spot reflection. In addition, the images used in these methods usually have limitations, such as the viewing angle.
Method: In the proposed algorithm, a stable method is offered to remove the effects of specular spot reflection in the pupil, and necessary preprocessing is done to detect the exact location of the pupil. An optimized color mapping algorithm is proposed and the mapping is calculated with the help of the LM algorithm to accurately determine the pupil boundary. This method does not impose any restrictions on the eye image and shape, and the angle of the pupil in the image can be in any shape and direction.
Results: The proposed method does not assume any specific model as the final pupil boundary (circle or oval) and is robust to noise and specular reflection factors as well. This method has been able to accurately detect the pupil boundary with the accuracy of 98.8% using UBIRIS dataset and 98% using the collected data by authors.
Conclusion: The method presented in this paper can be used to increase the accuracy in determining the internal and external border of the iris to diagnose diseases related to the pupil and iris, as well as identity identification based on iris tissue. |
| first_indexed | 2024-03-11T21:31:19Z |
| format | Article |
| id | doaj.art-e4bbf5ed8e6e4dab90c278fa518ff494 |
| institution | Directory Open Access Journal |
| issn | 2423-3870 2423-3498 |
| language | fas |
| last_indexed | 2024-03-11T21:31:19Z |
| publishDate | 2023-06-01 |
| publisher | Kerman University of Medical Sciences |
| record_format | Article |
| series | مجله انفورماتیک سلامت و زیست پزشکی |
| spelling | doaj.art-e4bbf5ed8e6e4dab90c278fa518ff4942023-09-27T09:07:29ZfasKerman University of Medical Sciencesمجله انفورماتیک سلامت و زیست پزشکی2423-38702423-34982023-06-01101117Pupil Detection for Automatic Diagnosis of Eye Diseases Using Optimized Color MappingRasul Kheyrolahi0Fatemeh Hoorali1Mohammad Hossein Sedaaghi2Hossein Ebrahimnezhad3 MSc. in Electrical Engineering, Department of Electrical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran Ph.D. in Electrical Engineering, Assistant Professor, Department of Electrical Engineering, Faculty of Electrical and Computer Engineering, Esfarayn University of Technology, Esfarayn, Iran Ph.D. in Electrical Engineering, Professor, Department of Electrical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran Ph.D. in Electrical Engineering, Professor, Department of Electrical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran Introduction: Pupil and iris disorders form an important category of eye diseases. Accurate segmentation of the pupil is the first and most important step in the automatic diagnosis of diseases related to the pupil and iris. Most of the existing methods do not have enough accuracy and are sensitive to the effects of noise and specular spot reflection. In addition, the images used in these methods usually have limitations, such as the viewing angle. Method: In the proposed algorithm, a stable method is offered to remove the effects of specular spot reflection in the pupil, and necessary preprocessing is done to detect the exact location of the pupil. An optimized color mapping algorithm is proposed and the mapping is calculated with the help of the LM algorithm to accurately determine the pupil boundary. This method does not impose any restrictions on the eye image and shape, and the angle of the pupil in the image can be in any shape and direction. Results: The proposed method does not assume any specific model as the final pupil boundary (circle or oval) and is robust to noise and specular reflection factors as well. This method has been able to accurately detect the pupil boundary with the accuracy of 98.8% using UBIRIS dataset and 98% using the collected data by authors. Conclusion: The method presented in this paper can be used to increase the accuracy in determining the internal and external border of the iris to diagnose diseases related to the pupil and iris, as well as identity identification based on iris tissue.http://jhbmi.ir/article-1-752-en.pdfdisease diagnosissegmentationirispupilcolor mapping |
| spellingShingle | Rasul Kheyrolahi Fatemeh Hoorali Mohammad Hossein Sedaaghi Hossein Ebrahimnezhad Pupil Detection for Automatic Diagnosis of Eye Diseases Using Optimized Color Mapping مجله انفورماتیک سلامت و زیست پزشکی disease diagnosis segmentation iris pupil color mapping |
| title | Pupil Detection for Automatic Diagnosis of Eye Diseases Using Optimized Color Mapping |
| title_full | Pupil Detection for Automatic Diagnosis of Eye Diseases Using Optimized Color Mapping |
| title_fullStr | Pupil Detection for Automatic Diagnosis of Eye Diseases Using Optimized Color Mapping |
| title_full_unstemmed | Pupil Detection for Automatic Diagnosis of Eye Diseases Using Optimized Color Mapping |
| title_short | Pupil Detection for Automatic Diagnosis of Eye Diseases Using Optimized Color Mapping |
| title_sort | pupil detection for automatic diagnosis of eye diseases using optimized color mapping |
| topic | disease diagnosis segmentation iris pupil color mapping |
| url | http://jhbmi.ir/article-1-752-en.pdf |
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