DETERMINATION OF HOMOGENE RAYS WITH MORPHOLOGICAL PROCESSES IN HARDWOOD AND SOFTWOOD

Yıl 2019, , 52 - 59, 25.03.2019

Halime Ergün

https://doi.org/10.21923/jesd.463819

Öz

In this study, homogeneous rays were
determined by using image processing methods in hardwood and softwood (Abies
alba Mill, Tilia platyphyllos Scop., Tilia cordata Mill, Betula alba, Juglans
regia L. Walnut, Ulmus scabra Mill). The rays that parenchyma
cells are come together, have a porous structure in tangential section. Since
the rays are composed of small parenchyma cells, large regions were removed
from the image after thresholding. The regions that high/width ratio was high
were deleted from the image. One of the morphological processes, the close
process was combined with the parenchyma cells after rays were determined.
Non-combining and non-rays regions were deleted. The expansion and filling
process determined the final shape of the rays. The results were given
statistically and visually. As a result of the analysis; maximum and minimum
rays were determined Ulmus scabra Mill. and Abies alba Mill. respectively.
Systems that find the rays automatically by utilizing image-processing techniques
appropriate to the structure to be determined ensure that the study of wood
anatomy is done in a short time and more easily.

Anahtar Kelimeler

Image processing, Wood anatomy, The homogeneous rays, Morphological processes

YAPRAKLI VE İĞNE YAPRAKLI AĞAÇLARDA HOMOJEN ÖZ IŞINLARIN MORFOLOJİK İŞLEMLERLE BELİRLENMESİ

Öz

Bu çalışmada, yapraklı ve iğne
yapraklı ağaçlarda (Abies alba Mill, Tilia platyphyllos Scop., Tilia cordata Mill,
Betula alba, Juglans regia L. Walnut, Ulmus scabra
Mill) bulunan homojen öz ışınları görüntü işleme metotları kullanılarak
belirlenmiştir. Öz ışınları paranşim hücrelerin bir araya gelmesiyle oluşur ve
teğet kesitte gözenekli bir yapıya sahiptir. Küçük paranşim hücrelerinden
oluştuğu için eşikleme işleminden sonra büyük bölgeler görüntüden
çıkarılmıştır. Yükseklik/genişlik oranı fazla olan bölgeler de
görüntüden silinmiştir. Morfolojik işlemlerden biri olan kapama işlemi ile
paranşim hücreleri birleştirilerek öz ışınları bulunmuştur. Birleşmeyen ve öz
ışını olamayan bölgeler silinmiştir. 
Genişleme ve doldurma işlemi ile öz ışınların
son şekli belirlenmiştir. Elde edilen sonuçlar görsel ve istatistiksel olarak
verilmiştir. Yapılan analiz sonucunda; en fazla ve en az öz ışını sırasıyla
Ulmus scabra Mill. ve Abies alba Mill. de tespit edilmiştir. Belirlenmek
istenen yapıya uygun görüntü işleme tekniklerinden faydalanılarak öz ışınları
otomatik olarak bulan sistemler, odun anatomisi çalışmalarının kısa sürede ve
daha kolay bir şekilde yapılmasını sağlamaktadır.

Anahtar Kelimeler

Görüntü işleme, Odun anatomisi, Morfolojik işlemler, Homojen öz ışını

Kaynakça

• Baas P & Scweingruber F.H. 1987. Ecological Trends İn The Wood Anatomy of Trees, Shrubs and Climbers from Europe. IAWA Bull.8: 245-275.
• Baggett D., Nakaya M., McAuliffe M., Yamaguchi T.P., Lockett S. 2005. Whole cell segmentation in solid tissue sections. Cytometry. Part A: The Journal of the International Society for Analytical Cytology 67: 137–143.
• Boztoprak, H., & Ergün, M.E., 2017. Yapraklı Ağaçlarda Trahe ve Liflerin Belirlenmesi. Gaziosmanpaşa Bilimsel Araştırma Dergisi, 6(2), 87-96.
• Brunel, G., Borianne, P., Subsol, G., Jaeger, M., & Caraglio, Y., 2014. Automatic identification and characterization of radial files in light microscopy images of wood. Annals of botany, 114(4), 829-840.
• Clair B., Gril J., Di Renzo F., Yamamoto H., Quignard F., 2007. Characterization of a gel in the cell wall to elucidate the paradoxical shrinkage of tension wood. Biomacromolecules 9: 494–498.
• Fourcaud T., Zhang X., Stokes A., Lambers H., Korner C., 2008. Plant growth modelling and applications: the increasing importance of plant architecture in growth models. Annals of Botany 101: 1053–1063.
• Gonzalez, R.C. and Woods, R.E., 2007. Digital Image Processing. Prentice Hall, 3rd edition.
• Hermanson, J.C. and Wiedenhoeft A.C., 2011. A brief review of machine vision in the context of automated wood identification systems. IAWA Journal, 32(2): 233–250.
• Hitz OM, Gartner H., Heinrich I., Monbaron M., 2008. Wood anatomical changes in roots of European ash (Fraxinus excelsior L.) after exposure. Dendrochronologia 25: 145–152.
• Jeacocke MB., Lovell BC., 1994. Amulti-resolution algorithmfor cytological image segmentation. Proceedings of the 1994 Second Australian and New Zealand Conference on Intelligent Information Systems, 1994. IEEE, 322–326
• Jones R., Bischof L., 1996. A graph–based segmentation of wood micrographs. Computing science and statistics 28. Proceedings in Computing Science and Statistics 28: 12–20
• Kennel, P., Subsol, G., Guéroult, M., Guéroult, M., & Borianne, P. 2010, July. Automatic identification of cell files in light microscopic images of conifer wood. In Image Processing Theory Tools and Applications (IPTA), 2010 2nd International Conference on (pp. 98-103). IEEE.
• Kribs, D. A., 1935. Salient lines of structural specialization in the wood rays of dicotyledons. Botanical Gazette, 96(3), 547-557.
• McInerney T., Terzopoulos D., 1999. T-Snakes: topology adaptive snakes. Medical Image Analysis 4: 840–845.
• Menon PKB, Sulaiman A., Choon LS., 1993. Structure and identifcation of Malayan woods. Malayan Forest Records No 25. Forest Research Institute Malaysia, Malaysia
• Merev N., 2003a. Odun Anatomisi, Karadeniz Üniversitesi Orman Fakültesi, genel yayın no:209, ISBN:975-6983-29-9, Trabzon.
• Merev N., 2003b. Odun Anatomisi ve Odun Tanıtımı, Karadeniz Üniversitesi Orman Fakültesi, ISBN:975-6983-30-2 Trabzon.
• Noshiro S., Suzuki M., 2001. Ontogenetic wood anatomy of tree and subtree species of Nepalse Rhododendron (Ericaceae) and characterization of shrubs species. Amer. J. Bot.88:560-569. IAWA bulletin, (9), 1,24-30.
• Pan, S., & Kudo, M., 2011. Segmentation of pores in wood microscopic images based on mathematical morphology with a variable structuring element. Computers and Electronics in Agriculture, 75(2), 250-260.
• Park, J., & Keller, J. M. 2001. Snakes on the watershed. IEEE Transactions on pattern analysis and machine intelligence, 23(10), 1201-1205.
• Quelhas P., Nieuwland J., Dewitte W., Mendonc¸a AM., Murray J., Campilho A., 2011. Arabidopsis thaliana automatic cell file detection and cell length estimation. In: Kamel M, Campilho A, eds. Image analysis and recognition. Berlin: Springer, 1–11.
• Timar, M. C., Gurau, L., Porojan, M., & Beldean, E., 2013. Microscopic identification of wood species an important step in furniture conservation. European Journal of Science and Theology, 9(4), 243-252.
• Travis AJ., Hirst DJ, Chesson A., 1996. Automatic classification of plant cells according to tissue type using anatomical features obtained by the distance transform. Annals of Botany 78: 325–331.
• Vincent L., Soille P., 1991. Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence 13: 583–598.
• Wagenfuhr, R., 2000. Holzatlas, Holzatlas. Leipzig, Fachbuchverlag Lepzig: 707. ISBN 3-446-21390-2.