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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

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  • 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.
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Bibliografia

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