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A Fuzzy Thresholding Segmentation for Plant Root CT Images Based on Genetic Algorithm

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  2007 ASAE Annual Meeting  073051.(doi:10.13031/2013.22946)
Authors:   Xiwen Luo, Xuecheng Zhou
Keywords:   Genetic algorithm, Fuzzy Thresholding, CT images, Root in situ

The precise segmentation of roots CT images is the basis to implement the 3D reconstruction and quantitative analysis of plant root system in situ. The problem existing in the CT images segmentation for plant roots in situ were discussed. In order to segment plant root CT images with the inherent indistinction, a fuzzy thresholding algorithm was implemented with the criterion of maximum fuzzy entropy and genetic algorithm. The initial thresholds were obtained with histogram analysis. The CT images were divided into several different regions fuzzily through designing a simple fuzzy membership function. And according to the criterion of maximum fuzzy entropy, a genetic algorithm was used to determine the best thresholds of CT images segmentation. The programming test result showed that the algorithm was effective to improve the precision and efficiency of root CT images segmentation.

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