Application of the Fuzzy C-Means Clustering Algorithm on the Analysis of Medical Images
模糊C-均值聚类法在医学图像分析中的应用

In this paper, an improved method is proposed based on the Fuzzy C-means method to deal with medical images. This method includes three steps. The first step is the fuzzy pixels process in which a redundant image is built by FEV (fuzzy expectation value). The second step is the procession of FCM (fuzzy C-means clustering) with original images and their redundant images. The last step is the display of 3D model. This algorithm improves the accuracy of clustering as the redundant image increases the feature of pixels. Several results of medical images are exhibited including CT, spiral CT and MRI, which are processed with the 3D MIPA system developed by the authors. Because better segmentation results have been obtained, the system can represent the anatomy structure of bones and the bones in the joint based on recognition and 3D reconstruction.