基于K-means聚类的植物叶片图像叶脉提取  

Extracting vein of leaf image based on K-means clustering

作　　者：李灿灿[1] 王宝[1] 王静[1] 李丰果[1] 

机构地区：[1]华南师范大学物理与电信工程学院,广州510006

出　　处：《农业工程学报》2012年第17期157-162,共6页Transactions of the Chinese Society of Agricultural Engineering

摘　　要：植物的叶片是植物最基本、最主要的生命活动场所。叶脉的提取与分析对叶片和整株植物结构的分析有一定的应用价值。该文提出一种基于K-means聚类(clustering)的叶脉提取算法。该算法首先对叶片图像的HSI彩色空间中的I信息进行K-means聚类处理,根据聚类的结果提取叶片边界,并将叶片图像分为受光均匀和受光不均匀的2类。对于受光均匀的叶片图像在聚类结果中直接提取叶脉,而受光不均匀的叶片图像则需去除部分叶肉后再进行一次K-means聚类提取叶脉。结果表明:该算法能有效地降低叶脉提取的错分率。Leaf is the primary part of a plant and the major site of food production for the plant.Leaf vein extraction and analysis are useful for investigation of leaf and plant structures.In this paper,a vein extraction algorithm based on the K-means clustering is proposed.Using intensity information,K-means clustering is carried out.According to the clustering results,the boundary of the leaf is extracted and leaf images are divided into two types,the uniform illumination leaf image and the nonuniform illumination leaf image.For a uniform illumination leaf image,vein is directly extracted based on the clustering results.However,for the nonuniform illumination leaf image,some mesophylls are removed first,and K-means clustering is then used to extract the vein.The results show that the proposed algorithm can greatly reduce the misclassification error rate.

关 键 词：图像处理 图像分割 聚类算法 HSI彩色空间 叶脉提取 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] S126[自动化与计算机技术—计算机科学与技术]