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.
When a person watches different marrow-cell images he or she can identify every type of cells easily. In this process, human’s visual system has ability to adapt the different shades of the color marrow cells images. We propose a segmentation method for marrow-cell images based on fuzzy c-means clustering (FCM). Firstly, the count of cluster is calculated out using the shades of the R-matrix of a RGB formatted marrow cells image. Secondly, the fuzzy c-means clustering method is done on the R-matrix. Finally, the pixel of G-matrix and B-matrix are divided into some clusters by “one to one correspondence” of the position of pixels that belong to R-matrix, G-matrix or B-matrix. This paper’s contribution could be summarized into three points: 1) a frame work of the fuzzy c-means clustering for marrow-cell images segmentation is proposed. 2) Using FCM and the R- matrix component of a RGB formatted marrow-cell images to generate the count of clustering. 3) This method could adaption different shades of different marrow-cell images.
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.
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