基于K 均值聚类的小麦腥黑穗病菌冬孢子图像分割
Image Segmentation of Wheat Bunt Teliospores Based on K-Means Clustering
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摘要: 图像分析技术能够实现对小麦腥黑穗病菌冬孢子特征的定量分析，冬孢子区域的分割则是分析的前提.针对小麦腥黑穗病菌图像的特点，考虑到传统分割方法的局限性，提出了一种基于彩色图像的分割算法.研究以病菌彩色图像 B 分量为聚类对象，以 R、G、B 分量值之和不变为迭代终止条件，利用 K 均值聚类的方法分割病菌图像，使类内像素均值的距离和取得局部极小值.与其他分割方法进行比较的结果表明，该分割算法既分割出亮度不均匀的背景，对噪声的敏感度较小，又减少了分割后的冬孢子粘连，分割出的冬孢子数目增加.Abstract: In order to study the features of wheat bunt teliospores to be researched quantitatively through image analysis technique, the region of teliospores should be segmented beforehand. In view of the limitation of traditional segmentation methods, a kind of K-means clustering algorithm for colorized image segmentation was proposed according to the characteristics of wheat bunt. B component was chosen as segmentation clustering features, and the termination condition of iteration was the invariance of the sum of R, G and B components for the sum of distances between intra-class pixels to reach its local minimal. An experiment was conducted for wheat bunt teliospores segmentation and the results showed that the proposed algorithm was superior to other traditional methods since it was capable of filtering asymetrical background or impurities, reducing teliospore aherences and increasing the number of segmented teliospores.
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Keywords:
- wheat bunt /
- image segment /
- K-means clustering /
- teliospores
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