Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.639911
Title: Extracting root system architecture from X-ray micro computed tomography images using visual tracking
Author: Mairhofer, Stefan
ISNI:       0000 0004 5366 0344
Awarding Body: University of Nottingham
Current Institution: University of Nottingham
Date of Award: 2014
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Abstract:
X-ray micro computed tomography (µCT) is increasingly applied in plant biology as an imaging system that is valuable for the study of root development in soil, since it allows the three-dimensional and non-destructive visualisation of plant root systems. Variations in the X-ray attenuation values of root material and the overlap in measured intensity values between roots and soil caused by water and organic matter represent major challenges to the extraction of root system architecture. We propose a novel technique to recover root system information from X-ray CT data, using a strategy based on a visual tracking framework embedding a modiffed level set method that is evolved using the Jensen-Shannon divergence. The model-guided search arising from the visual tracking approach makes the method less sensitive to the natural ambiguity of X-ray attenuation values in the image data and thus allows a better extraction of the root system. The method is extended by mechanisms that account for plagiatropic response in roots as well as collision between root objects originating from different plants that are grown and interact within the same soil environment. Experimental results on monocot and dicot plants, grown in different soil textural types, show the ability of successfully extracting root system information. Various global root system traits are measured from the extracted data and compared to results obtained with alternative methods.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.639911  DOI: Not available
Keywords: QA 75 Electronic computers. Computer science ; QK640 Plant anatomy
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