基于核密度估计和K均值聚类算法的骨扫描图像分割

目的:探讨K均值聚类的改进算法,并将其应用于全身骨扫描图像的分割?方法:首先对二维全身骨SPECT图像进行锐化?平滑?灰度变换等预处理;其次用核密度估计方法拟合出图像像素概率密度函数曲线,根据曲线的峰值点确定K个初始聚类中心值;再应用K均值聚类对图像进行分割;最后使用模板匹配排除误识别的区域?结果:图像预处理凸显了感兴趣目标,并改善了图像质量;基于核密度估计的K均值聚类算法的Tanimoto相似度系数明显优于传统K均值算法,平均耗时短于其他分割算法?结论:核密度估计有效地避免K均值聚类算法中初始聚类中心选取的盲目性,使聚类结果更为快速?准确?稳定?改进的K均值聚类算法对骨扫描图像分割效果显著,更便于对感兴趣区域进行定性?定量分析?

Segmentation of bone scintigraphy image via the K-means clusters with kernel density estimation

Objective:This paper proposed an improved K-means clustering algorithm based on kernel density estimation which is used for the automatic segmentation of whole-body bone scan image. Methods:First,we sharpened and smoothed the 2D SPECT whole-body scan image for preprocessing. Second,we used Gaussian kernel density estimation curve to obtain optimal clustering centers as the initial value of K-means clustering algorithm. And then,we segmented the image using the K-means clustering algorithm. Finally,the template match method was performed to delete wrong recognized areas. Results:Image preprocessing provided clearer and more detailed activity structures and improve image quality. The improved K-means clustering algorithm generated a higher degree of Tanimoto similarity than traditional K-means method,and it had a less running time than others. Conclusion:Kernel density estimation can effectively avoid the blindness of the initial clustering center selection in the K-means method,and make the clustering results more rapid,accurate and stable. The proposed algorithm is suitable for whole-body bone scan image segmentation which has important significance to the analysis of region of interest.