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Leaf image processing for wheat trichome counting

Type of output distribution: thrichome crossing numbers at different distanced from leaf (Crossing) or thrichome length (Length) distribution:
Type of measurement units: pixels (Pixel) or other (User):
Name of measurement units, if its type is user-defined (for example, mkm):
Width of image in user units to determine measurement scale:
Step of trichome length counting in units defined:
Number of bins (trichome counting at different distances) to estimate length distribution:
Image file (png,tif,bmp,jpeg and gif formats supported):


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Help information

The program input is leaf image file in TIFF/PNG/JPG/BMP/GIF format prepared according the protocol (LeafImageProtocol.pdf). The algorithm works in several steps. First, it finds the border between leaf (dark) and background (light) area on the image. Then it moves the border line from the leaf to the background area and counts number of crossings of the line with trichomes at various distances from the leaf surface. Using the number of crossings at various distances, it calculates the length distribution of the trichomes on the leaf. The distances (trichome length) measured in image pixels or user defined units (micrometers, millimeters etc.). In the latter case user should provide the unit name and the width of the image in this units to define the scale. User should also define the step of the border line moving from the leaf and the number of such steps (bins in the trichome length distribution). Note, the maximal distance from the leaf surface in analysis is (step length)x(number of bins). For example, if the units are pixels, the step length is 1 and the number of bins is 151, program will move border line pixel by pixel away from the leaf surface and will do this 151 times. The maximal length of trichome in pixels detected in this way is therefore 1x151 pixels.

The detailed description of the method in Russian could be found in the paper of Doroshkov et al “The use of the computer-based image processing to leaf hairinessin wheat Triticum aestivum L.” (2009) VOGIS Herald , v. 13, p.218- 226. (lhdetect_rus_paper.pdf)

See also presentation "Analysis of the leaf hairiness morphology in wheat: computational approaches using the image processing technique (link) from the Russian-French workshop “Genomics, Bioinformatics and Life Science Modeling”.

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