Segmentation of corneal optical coherence tomography images using Graph Search and Radon transform

Amr Elsawy; Mohamed Abdel-Mottaleb; Mohamed Abou Shousha

doi:10.1117/12.2513114

15 March 2019 Segmentation of corneal optical coherence tomography images using Graph Search and Radon transform

Amr Elsawy, Mohamed Abdel-Mottaleb, Mohamed Abou Shousha

Proceedings Volume 10949, Medical Imaging 2019: Image Processing; 109491O (2019) https://doi.org/10.1117/12.2513114
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Various common corneal eye diseases, such as dry eye, Fuchs endothelial dystrophy, Keratoconus and corneal graft rejection, can be diagnosed based on the changes in the thickness of corneal microlayers. Optical Coherence Tomography (OCT) technology made it possible to obtain high resolution corneal images that show the microlayered structures of the cornea. Manual segmentation is subjective and not feasible due to the large volume of obtained images. Existing automatic methods, used for segmenting corneal layer interfaces, are not robust and they segment few corneal microlayer interfaces. Moreover, there is no large annotated database of corneal OCT images, which is an obstacle towards the application of powerful machine learning methods such as deep learning for the segmentation of corneal interfaces. In this paper, we propose a novel segmentation method for corneal OCT images using Graph Search and Radon Transform. To the best of our knowledge, we are the first to develop an automatic segmentation method for the six corneal microlayer interfaces. The proposed method involves a novel image denoising method and an inner interfaces localization method. The proposed method was tested on 15 corneal OCT images. The images were randomly selected and manually segmented by two operators. Experimental results show that our method has a mean segmentation error of 3.87 ± 5.21 pixels (i.e. 5.81 ± 7.82μm) across all interfaces compared to the segmentation of the manual operators. The two manual operators have mean segmentation difference of 4.07 ± 4.71 pixels (i.e. 6.11 ± 7.07μm). The mean running time to segment all the corneal microlayer interfaces is 6.66 ± 0.22 seconds.

Conference Presentation

Citation Download Citation

Amr Elsawy, Mohamed Abdel-Mottaleb, and Mohamed Abou Shousha "Segmentation of corneal optical coherence tomography images using Graph Search and Radon transform", Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109491O (15 March 2019); https://doi.org/10.1117/12.2513114

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available