Abstract：Objective To propose a novel segmentation algorithm for the whole-body bone scan image
based on the Gaussian mixture model (GMM) which is used for the automatic recognition of the
lesion area. Methods First, we sharpened and smoothed the 2D SPECT whole-body scan image for
preprocessing. Second, Gaussian kernel density estimation was adapted to obtain the initial value of
the expectation-maximization (EM) algorithm by fitting the curve of probability density function. Then
we segmented the image using the GMM algorithm. Finally, the template match method was used to
eliminate the wrong recognized areas. Results From subjective evaluation, the presented segmentation
method can provide clearer and more detailed activity structures and improve the image quality.
Quantitatively experimental results indicate that the GMM algorithm can generate a higher degree
of Tanimoto similarity than other methods, and has a less running time. Conclusion Kernel density
estimation can effectively prevent the blindness of the initial value selection in the EM algorithm. Thus
the lesion areas will be segmented accurately by combination of the GMM and EM method. Therefore,
the proposed method is a feasible algorithm for the whole-body bone scan image segmentation.
徐磊a，孟庆乐b，杨瑞b，田书畅a，蒋红兵a. 基于高斯混合模型和核密度估计的全身
骨骼SPECT图像分割算法研究[J]. 中国医疗设备, 2016, 31(2): 48-51.
XU Leia, MENG Qing-leb,YANG Ruib, TIAN Shu-changa,JIANG Hong-binga. A Research on the Segmentation Algorithm for the Whole Body SPECT
Image via the Gaussian Mixture Model with Kernel Density Estimation. China Medical Devices, 2016, 31(2): 48-51.
