基于遗传算法的原位根系CT图像的模糊阈值分割

原位根系CT图像的精确分割是实现植物根系3维重建和定量分析的重要基础。为了对原位根系CT序列图像进行准确、有效的分割，针对原位根系CT序列图像固有的模糊性特征，设计了一种基于遗传算法的模糊多阈值图像分割方法。该方法首先通过直方图分析确定了原位根系3维分割的初始阈值范围；然后通过设计一种模糊隶属度函数, 将图像模糊划分为若干个不同的区域; 最后采用最大模糊熵准则，并借助遗传算法寻找确定了一组序列图像的最佳分割阈值。编程实验结果证实，该算法不仅能更加准确、有效地对植物根系原位CT序列图像进行分割，并可提高图像阈值分割的精度和效率。

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

The CT images segmentation is one of key technologies for the 3D reconstruction and quantitative analysis of plant root system in situ. In order to improve the precision and efficiency of images segmentation,in accordance with the inherent indistinction of CT images, 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 neighborhood function. And according to the criterion of maximum fuzzy entropy, a genetic algorithm was used to find out the best thresholds of CT images segmentation. The result of programming test shows that the algorithm is effective to improve the precision and efficiency of root CT images segmentation.