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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Bibliographic Details
Published in:International journal of biomedical imaging 2016-01, Vol.2016 (2016), p.1-11
Main Authors: Peyman, Alireza, Mehri Dehnavi, Alireza, Jorjandi, Sahar, Kazemian Jahromi, Mahdi, Kafieh, Raheleh, Rabbani, Hossein, Hajizadeh, Fedra
Format: Article
Language:English
Subjects:
Algorithms
Automation
Boundaries
Colleges & universities
Cornea
Cross sections
Endothelium
Image processing
Innovations
Medical examination
Medical imaging
Methods
Microscopy
Optical tomography
Performance evaluation
Research centers
Segmentation
Segments
Signal processing
Standard deviation
Three dimensional
Ultrasonic imaging
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Summary: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.
ISSN:1687-4188
1687-4196
DOI:10.1155/2016/1420230