基于K均值聚类的厚壁组织区域自动提取

桉树是我国南方的速生丰产林,区域提取是定量分析各种组织抑制桉树茎生根原因的重要前提。为此,介绍了基于K均值聚类的在桉树茎切片图像中自适应提取厚壁组织区域的图像处理技术。将桉树茎切片彩色图像转换为CIEL*a*b*彩色空间,用K均值聚类分析算法对描述颜色的a*和b*通道进行聚类分析,提取细胞厚壁,然后填充其中白色的细胞腔,构成完整的厚壁组织区域。实验结果表明,在CIEL*a*b*空间使用该算法可以获得较准确的实验结果。

Research on Extracting the Sclerenchyma Region in a Eucalyptus Image Based on K-means Clustering

A The Eucalyptus is a fast-growing and fertility tree in south China. The region extraction is very important to analyze quantification ally which kind of organization suppresses the Eucalyptus stem to take root. An image processing method of extracting the sclerenchyma Region from a Eucalyptus Image was introduced. The CIE L*a*b* was utilized to conduct image processing for Eucalyptus slice Color images which satisfies extensive color ranging. The a* and b* are selected and clustered by K-means. After extracting the thick cell wall, the white cell cavity was filled in and the Sclerenchyma Region was formed. An adaptive segmentation method using a K-means clustering algorithm was employed to take out the sclerenchyma Region of eucalyptus.