@inproceedings{WeberTersteggeHallingetal.1995,
  author    = {Weber, S. and Terstegge, Andreas and Halling, H. and Herzog, H. and Reinartz, R. and Reinhart, P. and Rongen, F. and M{\"u}ller-G{\"a}rtner, H.-W.},
  title     = {The design of an animal PET: flexible geometry for achieving optimal spatial resolution or high sensitivity},
  series = {Conference record / 1995 IEEE Nuclear Science Symposium and Medical Imaging, October 21 - 28, 1995, San Francisco ; vol. 2},
  booktitle = {Conference record / 1995 IEEE Nuclear Science Symposium and Medical Imaging, October 21 - 28, 1995, San Francisco ; vol. 2},
  publisher = {IEEE},
  address   = {Piscataway, NJ},
  organization    = {Institute of Electrical and Electronics Engineers},
  isbn      = {078033180X ; 0780331818 ; 0780331826},
  pages     = {1002 -- 1005},
  year      = {1995},
  language  = {en}
}
@article{ArinkinDigelPorstetal.2014,
  author    = {Arinkin, Vladimir and Digel, Ilya and Porst, Dariusz and Temiz Artmann, Ayseg{\"u}l and Artmann, Gerhard},
  title     = {Phenotyping date palm varieties via leaflet cross-sectional imaging and artificial neural network application},
  series = {BMC bioinformatics},
  volume    = {15},
  journal   = {BMC bioinformatics},
  number    = {55},
  issn      = {1471-2105},
  doi       = {10.1186/1471-2105-15-55},
  pages     = {1 -- 8},
  year      = {2014},
  abstract  = {Background True date palms (Phoenix dactylifera L.) are impressive trees and have served as an indispensable source of food for mankind in tropical and subtropical countries for centuries. The aim of this study is to differentiate date palm tree varieties by analysing leaflet cross sections with technical/optical methods and artificial neural networks (ANN). Results Fluorescence microscopy images of leaflet cross sections have been taken from a set of five date palm tree cultivars (Hewlat al Jouf, Khlas, Nabot Soltan, Shishi, Um Raheem). After features extraction from images, the obtained data have been fed in a multilayer perceptron ANN with backpropagation learning algorithm. Conclusions Overall, an accurate result in prediction and differentiation of date palm tree cultivars was achieved with average prediction in tenfold cross-validation is 89.1\% and reached 100\% in one of the best ANN.},
  language  = {en}
}
@article{SawadaNakazawaTakenagaetal.2014,
  author    = {Sawada, Kazuaki and Nakazawa, Hirokazu and Takenaga, Shoko and Hizawa, Takeshi and Futagawa, Masato and Dasai, Fumihiro and Sakurai, Takashi and Okumura, Koichi and Hattori, Toshiaki and Ishida, Makoto},
  title     = {Multimodal bioimage sensor},
  series = {IEICE transactions on fundamentals of electronics, communidations and computer sciences},
  volume    = {E97-A (2014)},
  journal   = {IEICE transactions on fundamentals of electronics, communidations and computer sciences},
  number    = {3},
  publisher = {IEICE},
  address   = {Tokyo},
  issn      = {0916-8508 (Print) ; 1745-1337 (Online)},
  doi       = {10.1587/transfun.E97.A.726},
  pages     = {726 -- 733},
  year      = {2014},
  abstract  = {To visualize the biochemical distribution two-dimensionally, we invented a solid-state-type ion image sensor that indicates the chemical activity of solutions and cells. The device, which consists of a CCD array covered with a functionalized membrane to detect charge accumulation, is highly sensitive to changes in the concentration and two-dimensional distribution of ions and biomaterials.},
  language  = {en}
}