Comparison of GP and SAP in the image-processing filter construction using pathology images

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Tomoyuki Hiroyasu and Sosuke Fujita and 
                 Akihito Watanabe and Mitsunori Miki and Maki Ogura and 
                 Manabu Fukumoto",
  title =        "Comparison of GP and SAP in the image-processing
                 filter construction using pathology images",
  booktitle =    "3rd International Congress on Image and Signal
                 Processing (CISP 2010)",
  year =         "2010",
  month =        "16-18 " # oct,
  volume =       "2",
  pages =        "904--908",
  abstract =     "In this paper, programming methods of constructing
                 filters for choosing target images from pathology
                 images are discussed. Automatic construction of these
                 filters would be very useful in the medical field.
                 Image processing filters can be expressed as tree
                 topology operations. Genetic Programming (GP) is an
                 evolutionary computation algorithm that can design tree
                 topology operations. Simulated Annealing Programming
                 (SAP) is also an emergent algorithm that can create
                 tree topology operations. These two algorithms, GP and
                 SAP, were applied to construct Image Processing Filters
                 and the characteristics of these two algorithms were
                 compared. The results indicated that GP has strong
                 search capability for finding the global optimum
                 solution. However, in the latter part of the search,
                 the diversity of solutions is lost and the program size
                 becomes large. This can be avoided by removing introns.
                 It is assumed that filters developed by GP have strong
                 robustness for other images. On the other hand, SAP
                 requires many iterations to find the optimum but the
                 program size is small. Filters developed by SAP are
                 relatively weak from the viewpoint of robustness for
                 other images.",
  keywords =     "genetic algorithms, genetic programming, GP, SAP,
                 image processing filter construction, medical image
                 processing, pathology images, simulated annealing
                 programming, medical image processing, simulated
  DOI =          "doi:10.1109/CISP.2010.5646895",
  notes =        "'GP can derive the best solution with less evaluation
                 time than SAP.' Also known as \cite{5646895}",

Genetic Programming entries for Tomoyuki Hiroyasu Sosuke Fujita Akihito Watanabe Mitsunori Miki Maki Ogura Manabu Fukumoto