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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2019
Subtitle of host publicationImage Processing
EditorsBennett A. Landman, Elsa D. Angelini, Elsa D. Angelini, Elsa D. Angelini
PublisherSPIE
ISBN (Electronic)9781510625457
DOIs
StatePublished - Jan 1 2019
EventMedical Imaging 2019: Image Processing - San Diego, United States
Duration: Feb 19 2019Feb 21 2019

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10949
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2019: Image Processing
CountryUnited States
CitySan Diego
Period2/19/192/21/19

Fingerprint

Radon
Optical tomography
Optical Coherence Tomography
radon
tomography
Pixels
Image denoising
operators
Image resolution
Grafts
eye diseases
Learning systems
pixels
Mathematical transformations
machine learning
cornea
Fuchs' Endothelial Dystrophy
Corneal Diseases
Keratoconus
rejection

Keywords

  • Cornea Segmentation
  • Graph Search
  • OCT
  • Radon Transform

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

Cite this

Elsawy, A., Abdel-Mottaleb, M., & Abou Shousha, M. (2019). Segmentation of corneal optical coherence tomography images using Graph Search and Radon transform. In B. A. Landman, E. D. Angelini, E. D. Angelini, & E. D. Angelini (Eds.), Medical Imaging 2019: Image Processing [109491O] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10949). SPIE. https://doi.org/10.1117/12.2513114

Segmentation of corneal optical coherence tomography images using Graph Search and Radon transform. / Elsawy, Amr; Abdel-Mottaleb, Mohamed; Abou Shousha, Mohamed.

Medical Imaging 2019: Image Processing. ed. / Bennett A. Landman; Elsa D. Angelini; Elsa D. Angelini; Elsa D. Angelini. SPIE, 2019. 109491O (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10949).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Elsawy, A, Abdel-Mottaleb, M & Abou Shousha, M 2019, Segmentation of corneal optical coherence tomography images using Graph Search and Radon transform. in BA Landman, ED Angelini, ED Angelini & ED Angelini (eds), Medical Imaging 2019: Image Processing., 109491O, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 10949, SPIE, Medical Imaging 2019: Image Processing, San Diego, United States, 2/19/19. https://doi.org/10.1117/12.2513114
Elsawy A, Abdel-Mottaleb M, Abou Shousha M. Segmentation of corneal optical coherence tomography images using Graph Search and Radon transform. In Landman BA, Angelini ED, Angelini ED, Angelini ED, editors, Medical Imaging 2019: Image Processing. SPIE. 2019. 109491O. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE). https://doi.org/10.1117/12.2513114
Elsawy, Amr ; Abdel-Mottaleb, Mohamed ; Abou Shousha, Mohamed. / Segmentation of corneal optical coherence tomography images using Graph Search and Radon transform. Medical Imaging 2019: Image Processing. editor / Bennett A. Landman ; Elsa D. Angelini ; Elsa D. Angelini ; Elsa D. Angelini. SPIE, 2019. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE).
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