Comparison and Supervised Learning of Segmentation Methods Dedicated to Specular Microscope Images of Corneal Endothelium
The cornea is the front of the eye. Its inner cell layer, called the endothelium, is important because it is closely related to the light transparency of the cornea. An in vivo observation of this layer is performed by using specular microscopy to evaluate the health of the cells: a high spatial den...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2014/704791 |
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| author | Yann Gavet Jean-Charles Pinoli |
| author_facet | Yann Gavet Jean-Charles Pinoli |
| author_sort | Yann Gavet |
| collection | DOAJ |
| description | The cornea is the front of the eye. Its inner cell layer, called the endothelium, is important because it is closely related to the light transparency of the cornea. An in vivo observation of this layer is performed by using specular microscopy to evaluate the health of the cells: a high spatial density will result in a good transparency. Thus, the main criterion required by ophthalmologists is the cell density of the cornea endothelium, mainly obtained by an image segmentation process. Different methods can perform the image segmentation of these cells, and the three most performing methods are studied here. The question for the ophthalmologists is how to choose the best algorithm and to obtain the best possible results with it. This paper presents a methodology to compare these algorithms together. Moreover, by the way of geometric dissimilarity criteria, the algorithms are tuned up, and the best parameter values are thus proposed to the expert ophthalmologists. |
| format | Article |
| id | doaj-art-54011ebb53c3434eb84414703f91d267 |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-54011ebb53c3434eb84414703f91d2672025-02-03T01:32:09ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962014-01-01201410.1155/2014/704791704791Comparison and Supervised Learning of Segmentation Methods Dedicated to Specular Microscope Images of Corneal EndotheliumYann Gavet0Jean-Charles Pinoli1LGF UMR CNRS 5307, École Nationale Supérieure des Mines de Saint-Etienne, 158 Cours Fauriel, 42023 Saint-Etienne Cedex 2, FranceLGF UMR CNRS 5307, École Nationale Supérieure des Mines de Saint-Etienne, 158 Cours Fauriel, 42023 Saint-Etienne Cedex 2, FranceThe cornea is the front of the eye. Its inner cell layer, called the endothelium, is important because it is closely related to the light transparency of the cornea. An in vivo observation of this layer is performed by using specular microscopy to evaluate the health of the cells: a high spatial density will result in a good transparency. Thus, the main criterion required by ophthalmologists is the cell density of the cornea endothelium, mainly obtained by an image segmentation process. Different methods can perform the image segmentation of these cells, and the three most performing methods are studied here. The question for the ophthalmologists is how to choose the best algorithm and to obtain the best possible results with it. This paper presents a methodology to compare these algorithms together. Moreover, by the way of geometric dissimilarity criteria, the algorithms are tuned up, and the best parameter values are thus proposed to the expert ophthalmologists.http://dx.doi.org/10.1155/2014/704791 |
| spellingShingle | Yann Gavet Jean-Charles Pinoli Comparison and Supervised Learning of Segmentation Methods Dedicated to Specular Microscope Images of Corneal Endothelium International Journal of Biomedical Imaging |
| title | Comparison and Supervised Learning of Segmentation Methods Dedicated to Specular Microscope Images of Corneal Endothelium |
| title_full | Comparison and Supervised Learning of Segmentation Methods Dedicated to Specular Microscope Images of Corneal Endothelium |
| title_fullStr | Comparison and Supervised Learning of Segmentation Methods Dedicated to Specular Microscope Images of Corneal Endothelium |
| title_full_unstemmed | Comparison and Supervised Learning of Segmentation Methods Dedicated to Specular Microscope Images of Corneal Endothelium |
| title_short | Comparison and Supervised Learning of Segmentation Methods Dedicated to Specular Microscope Images of Corneal Endothelium |
| title_sort | comparison and supervised learning of segmentation methods dedicated to specular microscope images of corneal endothelium |
| url | http://dx.doi.org/10.1155/2014/704791 |
| work_keys_str_mv | AT yanngavet comparisonandsupervisedlearningofsegmentationmethodsdedicatedtospecularmicroscopeimagesofcornealendothelium AT jeancharlespinoli comparisonandsupervisedlearningofsegmentationmethodsdedicatedtospecularmicroscopeimagesofcornealendothelium |