Extracting Rules for Cell Segmentation in Corneal Endothelial Cell Images Using GP
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gp-bibliography.bib Revision:1.8028
- @InProceedings{Hiroyasu:2013:SMC,
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author = "Tomoyuki Hiroyasu and Shunsuke Sekiya and
Sakito Nunokawa and Noriko Koizumi and Naoki Okumura and
Utako Yamamoto",
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booktitle = "IEEE International Conference on Systems, Man, and
Cybernetics (SMC 2013)",
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title = "Extracting Rules for Cell Segmentation in Corneal
Endothelial Cell Images Using GP",
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year = "2013",
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month = oct,
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pages = "1811--1816",
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keywords = "genetic algorithms, genetic programming, cell
segmentation, rule",
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DOI = "doi:10.1109/SMC.2013.305",
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abstract = "In tissue engineering of the corneal endothelium,
extracting feature values of cultured cells from cell
images helps us to automatically judge whether they are
transplantable. To extract feature values, accurate
image processing for cell segmentation is needed. We
previously proposed a method that constructs a
tree-structural image-processing filter by
automatically combining known image-processing filters.
In this paper, we propose a more accurate method that
can be applied to images in which statistics differ in
different regions. The proposed method prepares two
types of nodes. One type of node represents known
image-processing filters, and the other represents
conditional branches, which determine the divergent
direction using the statistics of the cell images.
Moreover, the proposed method optimises their
combination by using genetic programming (GP). The
proposed method is compared with the existing method
using GP and specialist software for analysing cell
images. The results show that the proposed method has
superior accuracy.",
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notes = "Also known as \cite{6722065}",
- }
Genetic Programming entries for
Tomoyuki Hiroyasu
Shunsuke Sekiya
Sakito Nunokawa
Noriko Koizumi
Naoki Okumura
Utako Yamamoto
Citations