Obtaining Thickness Maps of Corneal Layers Using the Optimal Algorithm for Intracorneal Layer Segmentation
Optical Coherence Tomography (OCT) is one of the most informative methodologies in ophthalmology and provides cross sectional images from anterior and posterior segments of the eye. Corneal diseases can be diagnosed by these images and corneal thickness maps can also assist in the treatment and diag...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2016/1420230 |
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| author | Hossein Rabbani Rahele Kafieh Mahdi Kazemian Jahromi Sahar Jorjandi Alireza Mehri Dehnavi Fedra Hajizadeh Alireza Peyman |
| author_facet | Hossein Rabbani Rahele Kafieh Mahdi Kazemian Jahromi Sahar Jorjandi Alireza Mehri Dehnavi Fedra Hajizadeh Alireza Peyman |
| author_sort | Hossein Rabbani |
| collection | DOAJ |
| description | Optical Coherence Tomography (OCT) is one of the most informative methodologies in ophthalmology and provides cross sectional images from anterior and posterior segments of the eye. Corneal diseases can be diagnosed by these images and corneal thickness maps can also assist in the treatment and diagnosis. The need for automatic segmentation of cross sectional images is inevitable since manual segmentation is time consuming and imprecise. In this paper, segmentation methods such as Gaussian Mixture Model (GMM), Graph Cut, and Level Set are used for automatic segmentation of three clinically important corneal layer boundaries on OCT images. Using the segmentation of the boundaries in three-dimensional corneal data, we obtained thickness maps of the layers which are created by these borders. Mean and standard deviation of the thickness values for normal subjects in epithelial, stromal, and whole cornea are calculated in central, superior, inferior, nasal, and temporal zones (centered on the center of pupil). To evaluate our approach, the automatic boundary results are compared with the boundaries segmented manually by two corneal specialists. The quantitative results show that GMM method segments the desired boundaries with the best accuracy. |
| format | Article |
| id | doaj-art-857fa68434b446788d1ba42359a927e1 |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-857fa68434b446788d1ba42359a927e12025-02-03T05:59:41ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962016-01-01201610.1155/2016/14202301420230Obtaining Thickness Maps of Corneal Layers Using the Optimal Algorithm for Intracorneal Layer SegmentationHossein Rabbani0Rahele Kafieh1Mahdi Kazemian Jahromi2Sahar Jorjandi3Alireza Mehri Dehnavi4Fedra Hajizadeh5Alireza Peyman6Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan 8174673461, IranDepartment of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan 8174673461, IranDepartment of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan 8174673461, IranStudent Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan 8174673461, IranDepartment of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan 8174673461, IranNoor Ophthalmology Research Center, Noor Eye Hospital, Tehran 1968653111, IranIsfahan University of Medical Sciences, Isfahan 817467346, IranOptical Coherence Tomography (OCT) is one of the most informative methodologies in ophthalmology and provides cross sectional images from anterior and posterior segments of the eye. Corneal diseases can be diagnosed by these images and corneal thickness maps can also assist in the treatment and diagnosis. The need for automatic segmentation of cross sectional images is inevitable since manual segmentation is time consuming and imprecise. In this paper, segmentation methods such as Gaussian Mixture Model (GMM), Graph Cut, and Level Set are used for automatic segmentation of three clinically important corneal layer boundaries on OCT images. Using the segmentation of the boundaries in three-dimensional corneal data, we obtained thickness maps of the layers which are created by these borders. Mean and standard deviation of the thickness values for normal subjects in epithelial, stromal, and whole cornea are calculated in central, superior, inferior, nasal, and temporal zones (centered on the center of pupil). To evaluate our approach, the automatic boundary results are compared with the boundaries segmented manually by two corneal specialists. The quantitative results show that GMM method segments the desired boundaries with the best accuracy.http://dx.doi.org/10.1155/2016/1420230 |
| spellingShingle | Hossein Rabbani Rahele Kafieh Mahdi Kazemian Jahromi Sahar Jorjandi Alireza Mehri Dehnavi Fedra Hajizadeh Alireza Peyman Obtaining Thickness Maps of Corneal Layers Using the Optimal Algorithm for Intracorneal Layer Segmentation International Journal of Biomedical Imaging |
| title | Obtaining Thickness Maps of Corneal Layers Using the Optimal Algorithm for Intracorneal Layer Segmentation |
| title_full | Obtaining Thickness Maps of Corneal Layers Using the Optimal Algorithm for Intracorneal Layer Segmentation |
| title_fullStr | Obtaining Thickness Maps of Corneal Layers Using the Optimal Algorithm for Intracorneal Layer Segmentation |
| title_full_unstemmed | Obtaining Thickness Maps of Corneal Layers Using the Optimal Algorithm for Intracorneal Layer Segmentation |
| title_short | Obtaining Thickness Maps of Corneal Layers Using the Optimal Algorithm for Intracorneal Layer Segmentation |
| title_sort | obtaining thickness maps of corneal layers using the optimal algorithm for intracorneal layer segmentation |
| url | http://dx.doi.org/10.1155/2016/1420230 |
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