An Efficient Algorithm Optimization of CT Images Segmentation Based on K-Means Clustering

Abstract

Computer tomography image (CT Image) segmentation algorithms have a number of advantages. However, most of these image segmentation algorithms suffer from long computation time because the number of pixels and the encoding parameters is large. We optimized the k-means clustering program with MATLAB language in order to improve the efficiency and stability of k-clustering algorithm in CT image segmentation. One hundred CT images are used to test the proposed method code and compare with the k-means function of the MATLAB R2012a Statistics Toolbox. We analyzed the difference of the two kinds program running time using single factor analysis of variance (ANOVA) and observed the efficiency and robustness of the segmentation results. The experimental results show that the optimized k-means clustering algorithm code has higher efficiency and robustness of segmentation. High performance of the proposed k-means clustering program is illustrated in terms of both the evaluation performance and computation time, compared with some current segmentation methods. It is empirically shown that the proposed k-means clustering program is robust and efficient for CT images segmentation.