目的 针对传统图像分割方法易受噪声干扰的影响，提出一种新的结合LPG&PCA（principal component analysis with local pixel grouping）的中智学图像分割方法。方法 该方法首先利用中智学集合理论把图像转化成中智学图像；然后建立LPG&PCA滤波模型，利用图像中不确定性元素信息，对图像进行α-LPG&PCA滤波运算和β-增强运算，使处理后的噪声点更加平滑；最后，利用γ-均值聚类方法进行分割。结果 实验结果表明，该算法可以有效地消除噪声，提高图像的峰值信噪比，在抗噪性、分割错误率等方面都有较佳的效果。结论 由于本文方法将中智学集合理论应用到图像分割中，充分利用了图像中的不确定性因素，从而提高了图像分割的精度。理论分析和实验结果表明了该算法的有效性。

Objective Given the interference from noise, it is difficult to segment noisy images by traditional image segmentation method. Therefore,we propose a new neutrosophic image segmentation method integrating LPG&PCA(principal component analysis with local pixel grouping). Method First,the input image is converted into neutrosophic images by using the neutrosophic set theory. Then,we filter the noise image by applying the proposed α-LPG&PCA filtering operation,and advance the denoised image by employing the β-enhancing operation,with the uncertainty information from the input image,to smooth the noisy points in the input image. Finally,we segment the denoised and enhanced image with a γ-means clustering method. Result The experimental results indicate that the proposed algorithm can eliminate noise effectively as well as improve the PSNR,therefore,our method can achieve desirable results in noise immunity and segmentation accuracy. Conclusion The accuracy of our algorithm is improved,through the neutrosophic set theory and the uncertainty information from the input image. Theoretical analysis and experimental results show the effectiveness of the proposed algorithm.