Segmentation of corneal optical coherence tomography images using randomized Hough transform

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

Abstract

Measuring the thickness of different corneal microlayers is important for the diagnosis of common corneal eye diseases such as dry eye, keratoconus, Fuchs endothelial dystrophy, and corneal graft rejection. High resolution corneal images, obtained using optical coherence tomography (OCT), made it possible to measure the thickness of different corneal microlayers in vivo. The manual segmentation of these images is subjective and time consuming. Therefore, automatic segmentation is necessary. Several methods were proposed for segmenting corneal OCT images, but none of these methods segment all the microlayer interfaces and they are not robust. In addition, the lack of a large annotated database of corneal OCT images impedes the application of machine learning methods such as deep learning which proves to be very powerful. In this paper, we present a new corneal OCT image segmentation algorithm using Randomized Hough Transform. To the best of our knowledge, we developed the first automatic segmentation method for the six corneal microlayer interfaces. The proposed method includes a robust estimate of relative distances of inner corneal interfaces with respect to outer corneal interfaces. Also, it handles properly the correct ordering and the non-intersection of corneal microlayer interfaces. The proposed method was tested on 15 corneal OCT images that were randomly selected. OCT images were manually segmented by two trained operators for comparison. Comparison with the manual segmentation shows that the proposed method has mean segmentation error of 3.77±4.25 pixels across all interfaces which corresponds to 5.66 ± 6.38μm. The mean segmentation error between the two manual operators is 4.07 ± 4.71 pixels, which corresponds to 6.11 ± 7.07μm. The proposed method takes a mean time of 2.59 ± 0.06 seconds to segment six corneal interfaces.

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

Hough transforms
Optical tomography
Optical Coherence Tomography
tomography
Pixels
eye diseases
pixels
Fuchs' Endothelial Dystrophy
operators
Corneal Diseases
machine learning
Image resolution
Image segmentation
Grafts
Keratoconus
Learning systems
Eye Diseases
Graft Rejection
rejection
learning

Keywords

  • Cornea Segmentation
  • OCT
  • Randomized Hough 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 randomized Hough transform. In B. A. Landman, E. D. Angelini, E. D. Angelini, & E. D. Angelini (Eds.), Medical Imaging 2019: Image Processing [109490U] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10949). SPIE. https://doi.org/10.1117/12.2512865

Segmentation of corneal optical coherence tomography images using randomized Hough 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. 109490U (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 randomized Hough transform. in BA Landman, ED Angelini, ED Angelini & ED Angelini (eds), Medical Imaging 2019: Image Processing., 109490U, 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/10.1117/12.2512865
Elsawy A, Abdel-Mottaleb M, Abou Shousha M. Segmentation of corneal optical coherence tomography images using randomized Hough transform. In Landman BA, Angelini ED, Angelini ED, Angelini ED, editors, Medical Imaging 2019: Image Processing. SPIE. 2019. 109490U. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE). https://doi.org/10.1117/12.2512865
Elsawy, Amr ; Abdel-Mottaleb, Mohamed ; Abou Shousha, Mohamed. / Segmentation of corneal optical coherence tomography images using randomized Hough 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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