Extracting Rules for Cell Segmentation in Corneal Endothelial Cell Images Using GP

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Tomoyuki Hiroyasu and Shunsuke Sekiya and 
                 Sakito Nunokawa and Noriko Koizumi and Naoki Okumura and 
                 Utako Yamamoto",
  booktitle =    "IEEE International Conference on Systems, Man, and
                 Cybernetics (SMC 2013)",
  title =        "Extracting Rules for Cell Segmentation in Corneal
                 Endothelial Cell Images Using GP",
  year =         "2013",
  month =        oct,
  pages =        "1811--1816",
  keywords =     "genetic algorithms, genetic programming, cell
                 segmentation, rule",
  DOI =          "doi:10.1109/SMC.2013.305",
  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.",
  notes =        "Also known as \cite{6722065}",

Genetic Programming entries for Tomoyuki Hiroyasu Shunsuke Sekiya Sakito Nunokawa Noriko Koizumi Naoki Okumura Utako Yamamoto