谱聚类集成的淋巴结超声图像分割算法

Research on Segmentation of Lymphatic Ultrasonic Images Based on the Spectral Cluster Ensemble

为了对低信噪比的超声图像进行有效分割,提出一种谱聚类集成的超声图像分割算法.首先用改进的全变差去噪模型对超声图像进行有效的去噪;然后用聚类集成的方法对去噪后的图像进行图像分割,基聚类器采用K均值算法,集成采用改进的谱聚类算法;最后用K均值算法对谱聚类集成的结果进行再次聚类,得到最终的集成聚类分割结果.实验结果表明,与现有的方法相比较,该算法分割效果更好.

A novel ultrasound image segmentation algorithm, which is based on the spectral cluster ensemble, is proposed to segment ultrasound images with low SNR. Firstly, the improved total variation model is used to eliminate noise in ultrasound. Then cluster ensemble approach which integrates K-means clusters and improved spectral cluster algorithm, is applied to segment ultrasound images. At last, the segmentation result is clustered again using K-means cluster to get the ultimate segmentation result. A large amount of experimental results have proved that our method outperforms many state-of-arts methods in the aspect of segmentation.