16 April 2014 Lactiferous vessel detection from microscopic cross-sectional images
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Proceedings Volume 9159, Sixth International Conference on Digital Image Processing (ICDIP 2014); 91591A (2014) https://doi.org/10.1117/12.2064389
Event: Sixth International Conference on Digital Image Processing, 2014, Athens, Greece
Abstract
This paper presents the methods to detect and segment lactiferous vessels or rubber latex vessels from gray scale microscopic cross-sectional images using polynomial curve-fitting with maximum and minimum stationary points. Polynomial curve-fitting is used to detect the location of lactiferous vessels from an image of a non-dyed cross-sectional slice which was taken by a digital camera through microscope lens. The lactiferous vessels are then segmented from an image using maximum and minimum stationary points with morphological closing operation. Two species of rubber trees of age between one to two years old are sampled namely, RRIM600 and RRIT251. Two data sets contain 30 microscopic cross-sectional images of one-year old rubber tree’s stems from each species are used in the experiments and the results reveal that most of the lactiferous vessel areas can be segmented correctly.
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Jirapath Jariyawatthananon, Nagul Cooharojananone, Rajalida Lipikorn, "Lactiferous vessel detection from microscopic cross-sectional images", Proc. SPIE 9159, Sixth International Conference on Digital Image Processing (ICDIP 2014), 91591A (16 April 2014); doi: 10.1117/12.2064389; https://doi.org/10.1117/12.2064389
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