Image segmentation is an active research topic in image processing. The Fuzzy C-means (FCM) clustering analysis has been widely used in image segmentation. As there is a large amount of delicate tissues such as blood vessels and nerves in medical images, noise generated during imaging process can easily affect successful segmentation of these tissues. The traditional FCM algorithm is not ideal for segmentation of images containing strong noise. In this study, we proposed an improved FCM algorithm with anti-noise capability. We first discussed the algorithm of dictionary learning for noise reduction. Then we developed a new image segmentation algorithm as a combination of the dictionary learning for noise reduction and the improved fuzzy C-means clustering. Lastly we used the algorithm of the improved FCM to segment images, during which we removed the non-target areas making use of the grayscale features of images and extracted accurately the areas of interests. The algorithm was tested using synthetic Shepp-Logan images and real medical magnetic resonance imaging (MRI) and computed tomography (CT) images. Compared to the synthetic data and real medical images segmented by the fuzzy C-means (FCM) clustering algorithm, the Kernel Fuzzy C-mean (KFCM) clustering algorithm, spectral clustering algorithm, the sparse learning based fuzzy C-means (SL_FCM) clustering algorithm, and the modified spatial KFCM (MSFCM) algorithm, the images segmented by the dictionary learning Fuzzy C-mean clustering (DLFCM) algorithm have higher partition coefficient, lower partition entropy, better visual perception, better clustering accuracy, and clustering purity.

图像分割是图像处理中一个活跃的研究主题。模糊C均值（FCM）聚类分析已广泛应用于图像分割中。由于医学图像中存在大量的脆弱组织，例如血管和神经，因此在成像过程中产生的噪声很容易影响这些组织的成功分割。传统的FCM算法对于分割包含强噪声的图像并不理想。在这项研究中，我们提出了一种具有抗噪能力的改进的FCM算法。我们首先讨论了字典学习的降噪算法。然后，我们开发了一种新的图像分割算法，该算法结合了字典学习以减少噪声和改进的模糊C均值聚类。最后，我们使用改进的FCM算法对图像进行分割，在此期间，我们利用图像的灰度特征删除了非目标区域，并准确提取了感兴趣的区域。使用合成的Shepp-Logan图像，真实医学磁共振成像（MRI）和计算机断层扫描（CT）图像对算法进行了测试。与通过模糊C均值（FCM）聚类算法分割的合成数据和实际医学图像相比，内核模糊C均值（KFCM）聚类算法，频谱聚类算法，基于稀疏学习的模糊C均值（SL_FCM）聚类字典学习模糊C均值聚类（DLFCM）算法对图像进行分割后，图像具有更高的分配系数，更低的分配熵，更好的视觉感知，更好的聚类精度和聚类纯度。