特征提取与聚类分析相结合的图像分割方法可以用于近红外显微图像化学信息的快速提取。针对基于主成分分析(PCA)的特征提取运算较为复杂的缺点，提出了一种加权二维主成分分析（W2DPCA）光谱特征提取方法，与模糊C均值(FCM)算法相结合用于近红外显微图像化学分布信息提取。通过片剂的近红外显微图像的仿真实验，验证了W2DPCA-FCM方法的可行性和有效性。实验结果表明，W2DPCA-FCM方法可以减少计算时间、提高聚类精度，是一种有效的红外显微图像分析方法。

Segmentation of near-infrared (NIR) microscopic image by feature extraction and clustering analysis methods can be used for efficient extraction of chemical information. Due to the high computational complexity of principal component analysis (PCA) in extracting features, we propose a weighted two-dimensional PCA (W2DPCA) spectral feature extraction scheme in this paper, which is combined with fuzzy C-mean (FCM) algorithm to extract the chemical information of NIR microscopic image. The feasibility and effectiveness of the proposed algorithm are verified by simulation experiments performed on NIR microscopic image of tablets. Experimental results show that W2DPCA-FCM is an effective infrared microscopy image analysis method since it can reduce the computation time and improve the clustering accuracy.