PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2012 | 22 | 3 | 669-684
Tytuł artykułu

Neural network segmentation of images from stained cucurbits leaves with colour symptoms of biotic and abiotic stresses

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The increased production of Reactive Oxygen Species (ROS) in plant leaf tissues is a hallmark of a plant's reaction to various environmental stresses. This paper describes an automatic segmentation method for scanned images of cucurbits leaves stained to visualise ROS accumulation sites featured by specific colour hues and intensities. The leaves placed separately in the scanner view field on a colour background are extracted by thresholding in the RGB colour space, then cleaned from petioles to obtain a leaf blade mask. The second stage of the method consists in the classification of within mask pixels in a hue-saturation plane using two classes, determined by leaf regions with and without colour products of the ROS reaction. At this stage a two-layer, hybrid artificial neural network is applied with the first layer as a self-organising Kohonen type network and a linear perceptron output layer (counter propagation network type). The WTA-based, fast competitive learning of the first layer was improved to increase clustering reliability. Widrow-Hoff supervised training used at the output layer utilises manually labelled patterns prepared from training images. The generalisation ability of the network model has been verified by K-fold cross-validation. The method significantly accelerates the measurement of leaf regions containing the ROS reaction colour products and improves measurement accuracy.
Rocznik
Tom
22
Numer
3
Strony
669-684
Opis fizyczny
Daty
wydano
2012
otrzymano
2011-09-12
poprawiono
2012-03-18
Twórcy
  • Institute of Applied Computer Science, Łódź University of Technology, Stefanowskiego 18/22, 90-924 Łódź, Poland
  • Institute of Applied Computer Science, Łódź University of Technology, Stefanowskiego 18/22, 90-924 Łódź, Poland
  • Department of Plant Physiology and Biochemistry, University of Łódź, Banacha 12/16, 90-237 Łódź, Poland
Bibliografia
  • Cheeseman, J.M. (2006). Hydrogen peroxide concentrations in leaves under natural conditions, Journal of Experimental Botany 57(10): 2435-2444.
  • Du Buf, H. and Bayer, M. (Eds.) (2002). Automatic Diatom Identification, World Scientific Publishing, New York, NY/London/Singapore/Hong Kong.
  • Forgy, E. (1965). Cluster analysis of multivariate data: Efficiency vs. interpretability of classification, Biometrics 21: 768-769.
  • Fukunaga, K. and Hostetler, L. (1975). The estimation of the gradient of a density function, with applications in pattern recognition, IEEE Transactions on Information Theory 21(1): 32-40.
  • Gonzalez, R.C. and Woods, R.E. (2008). Digital Image Processing, 3rd Edn., Prentice Hall, Upper Saddle River, NJ.
  • Hagan, M.T., Demuth, H. B. and Beale, M.H. (2009). Neural Network Design, University of Colorado, Denver, CO, Chapter 10, http://www.personeel.unimaas. nl/westra/Education/ANO/10widrow_hoff.pdf
  • Hecht-Nielsen, R. (1987). Counterpropagation networks, Applied Optics 26(23): 4979-4984.
  • Huang, CX-S., Liu, J-H. and Chen, X-J. (2010). Overexpression of PtrABF gene, a bZIP transcription factor isolated from Poncirus trifoliata, enhances dehydration and drought tolerance in tobacco via scavenging ROS and modulating expression of stress-responsive genes, BMC Plant Biology 10: 1-18, paper 230, http://www.biomedcentral.com/1471-2229/10/230.
  • James, W.C. (1971). An illustrated series of assessment keys for plant diseases, their preparation and usage, Canadian Plant Disease Survey 51(2): 39-65.
  • Kohonen, T. (1990). The self-organising map, Proceedings of IEEE 78(9): 1464-1479.
  • Kohonen, T. (2001). Self-organizing Maps, 3rd Edn., Springer-Verlag, Berlin/Heidelberg/New York, NY.
  • Masters, T. (1993) Practical Neural Network Recipes in C++, Academic Press Inc., San Diego, CA.
  • Ong, S., Yeo, N., Lee, K., Venkatesh, Y. and Cao, D. (2002). Segmentation of color images using a two-stage selforganizing network, Image and Vision Computing 20(4): 279-289.
  • Osowski, S. (2006). Neural Networks for Information Processing, Warsaw University of Technology Press, Warsaw, (in Polish).
  • Otsu, N. (1979). A threshold selection method from gray-level histograms, IEEE Transactions on Systems, Man, and Cybernetics 9(1): 62-66.
  • Refaeilzadeh, P., Tang, L. and Liu, H., (2009). Cross-validation, Encyclopedia of Database Systems, Springer Publishing Company, New York, NY, pp. 532-538,
  • Rubner, Y., Puzicha, J., Tomasi, C. and Buhmann J.M. (2001). Empirical evaluation of dissimilarity measures for color and texture, Computer Vision and Image Understanding 84(1): 25-43.
  • Sanders, J. and Kandrot, E. (2011). CUDA by Example: An Introduction to General-Purpose GPU Programming, Addison-Wesley, New York, NY/London.
  • Smith, A.R. (1978). Color gamut transform pairs, Computer Graphics 12(3): 12-19.
  • Soukupova, J. and Albrechtova, J. (2003). Image analysis-Tool for quantification of histochemical detection of phenolic compounds, lignin and peroxidases in needles of Norway spruce, Biologia Plantarum 46(4): 595-601.
  • Tabrizi, P.R., Rezatofighi, S.H. and Yazdanpanah, M.J. (2010). Using PCA and LVQ neural network for automatic recognition of five types of white blood cells, Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE, Teheran, Iran, pp. 5593-5596.
  • Tang, Q-H., Liu, B-H., Chen, Y-Q., Zhou, X-H. and Ding, JS. (2007). Application of LVQ neural network combined with the genetic algorithm in acoustic seafloor classification, Chinese Journal Geophysics 50(1): 313-319.
  • The Mathworks Inc. (2011a). MEX-files guide, http://www.mathworks.com/support/tech-notes/1600/1605.html
  • The Mathworks Inc. (2011b). Image processing toolbox user's guide, http://www.mathworks.com//help/toolbox/images/
  • Thordal-Christensen, H., Zhang, Z., Wei, Y.D. and Collinge, D.B. (1997). Subcellular localization of H₂O₂ in plants. H₂O₂ accumulation in papillae and hypersensitive response during the barley-powdery mildew interaction, Plant Journal 11(6): 1187-1194.
  • Unger, Ch., Kleta, S., Jandl, G. and Tiedemann, A. (2005). Suppression of the defence-related oxidative burst in bean leaf tissue and bean suspension cells by the necrotrophic pathogen Botrytus cinerea, Journal of Phytopathology 153(1): 15-26.
  • Wijekoon, C.P., Goodwin, P.H. and Hsiang, T. (2008). Quantifying fungal infection of plant leaves by digital image analysis using Scion Image software, Journal of Microbiological Methods 74(2-3): 94-101.
  • Widrow, B., Winter, R.G. and Baxter, R.A. (1988). Layered neural nets for pattern recognition, IEEE Transactions on Acoustics, Speech and Signal Processing 36(7): 1109-1118.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv22z3p669bwm
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.