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Year 2018, Volume 3, Issue 2, 225 - 233, 17.05.2018
https://doi.org/10.28978/nesciences.424679

Abstract

References

  • Bookstein, F.L. (1997). Landmark methods for forms without landmarks: morphometrics of group differences in outline shape. Medical Image Analysis, 1 (3), 225-43.
  • Breno, M., Leirs, H. & Van Dongen, S. (2011). Traditional and geometric morphometrics for studying skull morphology during growth in Mastomys natalensis (Rodentia: Muridae). Journal of Mammalogy, 92 (6), 1395-1406.
  • Duda, R. O., Hart, P. E., & Stork, D. G. (2012). Pattern classification. Wiley-Interscience Publication. John Wiley & Sons. 680p.
  • Hockaday, S., Beddow, T.A., Stone, M., Hancock, P., & Ross, L.G. (2000). Using truss networks to estimate the biomass of Oreochromis niloticus, and to investigage shape characteristics. Journal of Fish Biology, 57: 981-1000.
  • Hossain, M. A., Nahiduzzaman, M., Saha, D., Khanam, M. U. H. & Alam, M. S. (2010). Landmark-based morphometric and meristic variations of the endangered Carp, Kalibaus Labeo calbasu, from stocks of two isolated rivers, the Jamuna and Halda, and a hatchery. Zoological studies, 49 (4), 556-563.
  • İşçimen, B., Kutlu, Y. & Turan, C. (2017a). Classification of Serranidae Species Using Color Based Statistical Features. Natural and Engineering Sciences, 2 (1), 25-34.
  • İşçimen, B., Kutlu, Y. & Turan, C. (2017b). Classification of Fish Families Using Texture Analysis. The 3rd International Symposium on EuroAsian Biodiversity (SEAB-2017), 23.
  • İşçimen, B., Kutlu, Y. & Turan, C. (2017c). Performance Comparison of Different Sized Regions of Interest on Fish Classification. International Conference on Engineering Technologies, 222-227.
  • İşçimen, B., Kutlu, Y. & Turan, C. (2017d). Classification of Serranidae Species Using Color Based Statistical Features. Natural and Engineering Sciences 2 (1), 25-34
  • İşçimen, B., Kutlu, Y., Reyhaniye, AN., & Turan, C. (2014). Image analysis methods on fish recognition. 22nd Signal Processing and Communications Applications Conference, 1411-1414.
  • İşçimen, B., Kutlu, Y., Uyan, A. & Turan, C. (2015). Classification of fish species with two dorsal fins using centroid-contour distance. 23th Signal Processing and Communications Applications Conference. 1981-1984.
  • Klingenberg, C. P. (2011). MorphoJ: an integrated software package for geometric morphometrics. Molecular Ecology Resources, 11(2), 353-357.
  • Kutlu, Y. & Turan, C. (2017). The Landmarks Prediction for streaked gurnard Chelidonichthys Lastoviza using Artificial Neural Networks. Natural and Engineering Sciences, The 3rd International Symposium on EuroAsian Biodiversity (SEAB 2017), Belarus .
  • Kutlu, Y., İşçimen, B. & Turan, C. (2017a). Multi-Stage Fish Classification System Using Morphometry. Fresenius Environmental Bulletin, 26 (3), 1911-1917.
  • Kutlu, Y., Altan, G., İşçimen, B., Doğdu, S.A. & Turan, C. (2017b). Recognition of Species of Triglidae Family Using Deep Learning. Journal of the Black Sea/Mediterranean Environment, 23 (1), 56-65.
  • Kutlu, Y., İşçimen, B. & Turan, C. (2017c). Parameter Selection in Centroid-Contour Distance Method for Classification of Pufferfish Species. International Symposium on Pufferfish, 29.
  • Kutlu, Y., Kuntalp, M. & Kuntalp, D. (2009). Optimizing the performance of an MLP classifier for the automatic detection of epileptic spikes. Expert Systems with Applications, 36 (4), 7567-7575.
  • Parsons, K.J., Robinson, B.W. & Hrbek, T. (2003). Getting into shape: an empirical comparison of traditional truss-based morphometric methods with a newer geometric method applied to New World cichlids. Environmental Biology of Fishes, 67: 417-431.
  • Slice, D.E. (2007). Geometrics morphometrics. Annual Review of Anthropology, 36:261–81.
  • Strauss, R.E. & Bookstein, F.L. (1982). The truss: body form reconstructions in morphometrics. Systematic Zoology, 31: 113-135.
  • Turan, C. & Oral, M. (2005). A Computer Package Program for Morphometric Identifications of Fish Populations: MorFISH. ITAFE05: International Congress on Information Technology in Agriculture Food and Environment, Adana,12-14 Ekim, Proceedings Book Vol.1., 143-147.
  • Turan, C. (1999). A Note on The Examination of Morphometric Differentiation Among Fish Populations: The Truss System. Turkish Journal of Zoology, 23, 259-263.
  • Viscosi, V. & Cardini, A. (2011). Leaf morphology, taxonomy and geometric morphometrics: a simplified protocol for beginners. PloS one, 6 (10), e25630.
  • Zelditch, M.L., Debry, R.W. & Straney, D.O. (1989). Triangulation-measurement schemes in the multivariate analysis of size and shape. Journal of Mammalogy, 70: 571-579.

An Intelligent Software for Measurements of Biological Materials: BioMorph

Year 2018, Volume 3, Issue 2, 225 - 233, 17.05.2018
https://doi.org/10.28978/nesciences.424679

Abstract

Morphological characters have commonly been used in analysis of biological contexts. Researchers often use the arrangements of morphological landmarks in their studies to extract shape information from any biological materials and need to get bio-measurements using any computer aided tools. Getting landmarks and measurements from biological materials are a time-consuming process. Hence, this study is to provide an intelligent integrated software called BioMorph for morphological measurements. With the BioMorph, Family and species identification of a studied bio-object are automatically be determined using artificial neural network and k-nearest neighbor. The landmarks for discrimination of the bio-objects are automatically found from the given image using artificial neural network. In addition, network analysis methods such as the Euclid network distances, Truss network distances, Triangular network distances, some statistical measures such as mean, standard deviation, minimum and maximum values, etc. and image processing techniques such as image editing, image filtering, image segmentation, etc. are also integrated to the BioMorph.

References

  • Bookstein, F.L. (1997). Landmark methods for forms without landmarks: morphometrics of group differences in outline shape. Medical Image Analysis, 1 (3), 225-43.
  • Breno, M., Leirs, H. & Van Dongen, S. (2011). Traditional and geometric morphometrics for studying skull morphology during growth in Mastomys natalensis (Rodentia: Muridae). Journal of Mammalogy, 92 (6), 1395-1406.
  • Duda, R. O., Hart, P. E., & Stork, D. G. (2012). Pattern classification. Wiley-Interscience Publication. John Wiley & Sons. 680p.
  • Hockaday, S., Beddow, T.A., Stone, M., Hancock, P., & Ross, L.G. (2000). Using truss networks to estimate the biomass of Oreochromis niloticus, and to investigage shape characteristics. Journal of Fish Biology, 57: 981-1000.
  • Hossain, M. A., Nahiduzzaman, M., Saha, D., Khanam, M. U. H. & Alam, M. S. (2010). Landmark-based morphometric and meristic variations of the endangered Carp, Kalibaus Labeo calbasu, from stocks of two isolated rivers, the Jamuna and Halda, and a hatchery. Zoological studies, 49 (4), 556-563.
  • İşçimen, B., Kutlu, Y. & Turan, C. (2017a). Classification of Serranidae Species Using Color Based Statistical Features. Natural and Engineering Sciences, 2 (1), 25-34.
  • İşçimen, B., Kutlu, Y. & Turan, C. (2017b). Classification of Fish Families Using Texture Analysis. The 3rd International Symposium on EuroAsian Biodiversity (SEAB-2017), 23.
  • İşçimen, B., Kutlu, Y. & Turan, C. (2017c). Performance Comparison of Different Sized Regions of Interest on Fish Classification. International Conference on Engineering Technologies, 222-227.
  • İşçimen, B., Kutlu, Y. & Turan, C. (2017d). Classification of Serranidae Species Using Color Based Statistical Features. Natural and Engineering Sciences 2 (1), 25-34
  • İşçimen, B., Kutlu, Y., Reyhaniye, AN., & Turan, C. (2014). Image analysis methods on fish recognition. 22nd Signal Processing and Communications Applications Conference, 1411-1414.
  • İşçimen, B., Kutlu, Y., Uyan, A. & Turan, C. (2015). Classification of fish species with two dorsal fins using centroid-contour distance. 23th Signal Processing and Communications Applications Conference. 1981-1984.
  • Klingenberg, C. P. (2011). MorphoJ: an integrated software package for geometric morphometrics. Molecular Ecology Resources, 11(2), 353-357.
  • Kutlu, Y. & Turan, C. (2017). The Landmarks Prediction for streaked gurnard Chelidonichthys Lastoviza using Artificial Neural Networks. Natural and Engineering Sciences, The 3rd International Symposium on EuroAsian Biodiversity (SEAB 2017), Belarus .
  • Kutlu, Y., İşçimen, B. & Turan, C. (2017a). Multi-Stage Fish Classification System Using Morphometry. Fresenius Environmental Bulletin, 26 (3), 1911-1917.
  • Kutlu, Y., Altan, G., İşçimen, B., Doğdu, S.A. & Turan, C. (2017b). Recognition of Species of Triglidae Family Using Deep Learning. Journal of the Black Sea/Mediterranean Environment, 23 (1), 56-65.
  • Kutlu, Y., İşçimen, B. & Turan, C. (2017c). Parameter Selection in Centroid-Contour Distance Method for Classification of Pufferfish Species. International Symposium on Pufferfish, 29.
  • Kutlu, Y., Kuntalp, M. & Kuntalp, D. (2009). Optimizing the performance of an MLP classifier for the automatic detection of epileptic spikes. Expert Systems with Applications, 36 (4), 7567-7575.
  • Parsons, K.J., Robinson, B.W. & Hrbek, T. (2003). Getting into shape: an empirical comparison of traditional truss-based morphometric methods with a newer geometric method applied to New World cichlids. Environmental Biology of Fishes, 67: 417-431.
  • Slice, D.E. (2007). Geometrics morphometrics. Annual Review of Anthropology, 36:261–81.
  • Strauss, R.E. & Bookstein, F.L. (1982). The truss: body form reconstructions in morphometrics. Systematic Zoology, 31: 113-135.
  • Turan, C. & Oral, M. (2005). A Computer Package Program for Morphometric Identifications of Fish Populations: MorFISH. ITAFE05: International Congress on Information Technology in Agriculture Food and Environment, Adana,12-14 Ekim, Proceedings Book Vol.1., 143-147.
  • Turan, C. (1999). A Note on The Examination of Morphometric Differentiation Among Fish Populations: The Truss System. Turkish Journal of Zoology, 23, 259-263.
  • Viscosi, V. & Cardini, A. (2011). Leaf morphology, taxonomy and geometric morphometrics: a simplified protocol for beginners. PloS one, 6 (10), e25630.
  • Zelditch, M.L., Debry, R.W. & Straney, D.O. (1989). Triangulation-measurement schemes in the multivariate analysis of size and shape. Journal of Mammalogy, 70: 571-579.

Details

Primary Language English
Subjects Computer Science, Information System
Journal Section 3
Authors

Yakup KUTLU (Primary Author)
İSKENDERUN TEKNİK ÜNİVERSİTESİ


Cemal TURAN
İSKENDERUN TEKNİK ÜNİVERSİTESİ

Publication Date May 17, 2018
Application Date December 21, 2017
Acceptance Date May 4, 2018
Published in Issue Year 2018, Volume 3, Issue 2

Cite

APA Kutlu, Y. & Turan, C. (2018). An Intelligent Software for Measurements of Biological Materials: BioMorph . Natural and Engineering Sciences , 3 (2) , 225-233 . DOI: 10.28978/nesciences.424679

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