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 language | English (US) |
|---|---|
| Title of host publication | Medical Imaging 2019 |
| Subtitle of host publication | Image Processing |
| Editors | Bennett A. Landman, Elsa D. Angelini, Elsa D. Angelini, Elsa D. Angelini |
| Publisher | SPIE |
| ISBN (Electronic) | 9781510625457 |
| DOIs | |
| State | Published - Jan 1 2019 |
| Event | Medical Imaging 2019: Image Processing - San Diego, United States Duration: Feb 19 2019 → Feb 21 2019 |
Publication series
| Name | Progress in Biomedical Optics and Imaging - Proceedings of SPIE |
|---|---|
| Volume | 10949 |
| ISSN (Print) | 1605-7422 |
Conference
| Conference | Medical Imaging 2019: Image Processing |
|---|---|
| Country | United States |
| City | San Diego |
| Period | 2/19/19 → 2/21/19 |
Fingerprint
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
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 proceeding › Conference contribution
}
TY - GEN
T1 - Segmentation of corneal optical coherence tomography images using randomized Hough transform
AU - Elsawy, Amr
AU - Abdel-Mottaleb, Mohamed
AU - Abou Shousha, Mohamed
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
KW - Cornea Segmentation
KW - OCT
KW - Randomized Hough Transform
UR - http://www.scopus.com/inward/record.url?scp=85068311027&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85068311027&partnerID=8YFLogxK
U2 - 10.1117/12.2512865
DO - 10.1117/12.2512865
M3 - Conference contribution
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2019
A2 - Landman, Bennett A.
A2 - Angelini, Elsa D.
A2 - Angelini, Elsa D.
A2 - Angelini, Elsa D.
PB - SPIE
ER -