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
AN - SCOPUS:85068311027
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
T2 - Medical Imaging 2019: Image Processing
Y2 - 19 February 2019 through 21 February 2019
ER -