Ultrasound image segmentation based on multi-scale fuzzy c-means and particle swarm optimization

作者：Qi Zhang, Chaolun Li, Wenping Wang, Lijing Yang, Chunchun Huang · 年份：2012 · DOI：10.1049/cp.2012.2294 · 被引用次数：18 · 研究领域：Photoacoustic and Ultrasonic Imaging、Medical Image Segmentation Techniques

A multi-scale fuzzy c-means method integrated with particle swarm optimization (MsFCM-PSO) is proposed for ultrasound image segmentation. First, speckle reducing anisotropic diffusion is used to suppress noise in an ultrasound image and construct a series of images at multiple scales. Then the particle swarm optimization is incorporated into the multi-scale fuzzy c-means (MsFCM) to search for the global optima of cluster centers and update the membership of each pixel in a coarse-to-fine fashion. Finally, the image is segmented by assigning each pixel to the cluster with the highest membership. The method was validated on both synthetic and in vivo ultrasound images. It outperformed the traditional fuzzy c-means methods including MsFCM by 39.6% and 13.6%, in terms of the Pratt's figure of merit and segmentation accuracy, respectively. These results demonstrate that the MsFCM-PSO can provide an accurate tool for ultrasound image segmentation.