Optimization of K-Means Clustering Segmentation in Head CT Images

Abstract

We optimized the k-clustering program with MATLAB language in order to improve the stability and quality of k-clustering algorithm in medical CT image segmentation. One hundred and sixty-five head nodule thoracic computed tomography scans are used to test the proposed method 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 variance analysis method and observed the stability and quality of the images segmentation. The experimental results show that the optimized k-means clustering algorithm programming has higher stability and quality of segmentation. In the environment of Windows operation system and hardware of personal computer configuration, the segmenting times are about only one second, significantly lower than the original segmentation procedures. These can eliminate the feeling of waiting and improve the users comfort and efficiency.