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Segmentation of endothelial cells of the cornea from the distance map of confocal microscope images

Raidel Herrera Pereda (UGent) , Alberto Taboada Crispi, Danilo Babin (UGent) and Wilfried Philips (UGent)
(2021) COMPUTERS IN BIOLOGY AND MEDICINE. 139.
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Abstract
We propose a novel algorithm for segmenting cells of the cornea endothelium layer on confocal microscope images. To get an inter-cellular space with minimum gray-scale value and to enhance cell borders, we apply a difference of Gaussian filter before image binarization by thresholding with the minimum gray-scale value. Removal of segmented noise and artifacts is performed by automatic thresholding (using an image frequency analysis to obtain a global threshold value per image). Final segmentation of cells is achieved by fitting the largest inscribed circles into the centers of cell regions defined by the distance map of the binary images. Parameters of interest such as cell count and density, pleomorphism, polymegathism, and F-measure are computed on a publicly available data-set (Confocal Corneal Endothelial Microscopy Data Set - Rotterdam Ophthalmic Data Repository) and compared against the results of the segmentation methods included with the data set, and the results of state of the art automatic methods. The obtained results achieve higher accuracy compared to the results of the segmentation included with the data set (e.g., -proposed versus dataset in R-2 and mean relative error-, cell count: 0.823, - 0.241 versus 0.017, 0.534; cell density: 0.933, - 0.067 versus 0.154, 0.639; cell polymegathism: 0.652, - 0.079 versus 0.075, 0.886; cell pleomorphism: 0.242, - 0.128 versus 0.0352, - 0.222, respectively), and are in good agreement with the results of the state of the art method.
Keywords
Health Informatics, Computer Science Applications, Corneal endothelial cells, Confocal microscopy images, Image frequency spectrum, Distance map, Segmentation, AUTOMATIC ESTIMATION, SYSTEM, DENSITY

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Citation

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MLA
Herrera Pereda, Raidel, et al. “Segmentation of Endothelial Cells of the Cornea from the Distance Map of Confocal Microscope Images.” COMPUTERS IN BIOLOGY AND MEDICINE, vol. 139, 2021, doi:10.1016/j.compbiomed.2021.104953.
APA
Herrera Pereda, R., Crispi, A. T., Babin, D., & Philips, W. (2021). Segmentation of endothelial cells of the cornea from the distance map of confocal microscope images. COMPUTERS IN BIOLOGY AND MEDICINE, 139. https://doi.org/10.1016/j.compbiomed.2021.104953
Chicago author-date
Herrera Pereda, Raidel, Alberto Taboada Crispi, Danilo Babin, and Wilfried Philips. 2021. “Segmentation of Endothelial Cells of the Cornea from the Distance Map of Confocal Microscope Images.” COMPUTERS IN BIOLOGY AND MEDICINE 139. https://doi.org/10.1016/j.compbiomed.2021.104953.
Chicago author-date (all authors)
Herrera Pereda, Raidel, Alberto Taboada Crispi, Danilo Babin, and Wilfried Philips. 2021. “Segmentation of Endothelial Cells of the Cornea from the Distance Map of Confocal Microscope Images.” COMPUTERS IN BIOLOGY AND MEDICINE 139. doi:10.1016/j.compbiomed.2021.104953.
Vancouver
1.
Herrera Pereda R, Crispi AT, Babin D, Philips W. Segmentation of endothelial cells of the cornea from the distance map of confocal microscope images. COMPUTERS IN BIOLOGY AND MEDICINE. 2021;139.
IEEE
[1]
R. Herrera Pereda, A. T. Crispi, D. Babin, and W. Philips, “Segmentation of endothelial cells of the cornea from the distance map of confocal microscope images,” COMPUTERS IN BIOLOGY AND MEDICINE, vol. 139, 2021.
@article{8725033,
  abstract     = {{We propose a novel algorithm for segmenting cells of the cornea endothelium layer on confocal microscope images. To get an inter-cellular space with minimum gray-scale value and to enhance cell borders, we apply a difference of Gaussian filter before image binarization by thresholding with the minimum gray-scale value. Removal of segmented noise and artifacts is performed by automatic thresholding (using an image frequency analysis to obtain a global threshold value per image). Final segmentation of cells is achieved by fitting the largest inscribed circles into the centers of cell regions defined by the distance map of the binary images. Parameters of interest such as cell count and density, pleomorphism, polymegathism, and F-measure are computed on a publicly available data-set (Confocal Corneal Endothelial Microscopy Data Set - Rotterdam Ophthalmic Data Repository) and compared against the results of the segmentation methods included with the data set, and the results of state of the art automatic methods. The obtained results achieve higher accuracy compared to the results of the segmentation included with the data set (e.g., -proposed versus dataset in R-2 and mean relative error-, cell count: 0.823, - 0.241 versus 0.017, 0.534; cell density: 0.933, - 0.067 versus 0.154, 0.639; cell polymegathism: 0.652, - 0.079 versus 0.075, 0.886; cell pleomorphism: 0.242, - 0.128 versus 0.0352, - 0.222, respectively), and are in good agreement with the results of the state of the art method.}},
  articleno    = {{104953}},
  author       = {{Herrera Pereda, Raidel and Crispi, Alberto Taboada and Babin, Danilo and Philips, Wilfried}},
  issn         = {{0010-4825}},
  journal      = {{COMPUTERS IN BIOLOGY AND MEDICINE}},
  keywords     = {{Health Informatics,Computer Science Applications,Corneal endothelial cells,Confocal microscopy images,Image frequency spectrum,Distance map,Segmentation,AUTOMATIC ESTIMATION,SYSTEM,DENSITY}},
  language     = {{eng}},
  pages        = {{19}},
  title        = {{Segmentation of endothelial cells of the cornea from the distance map of confocal microscope images}},
  url          = {{http://doi.org/10.1016/j.compbiomed.2021.104953}},
  volume       = {{139}},
  year         = {{2021}},
}
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