Application of Principal Component Analysis and Fuzzy C-Means Clustering Algorithm to the Classification of UHMWPE Wear Debris from Artificial Joints

The application of principal component analysis and fuzzy C-means clustering algorithm to the classification of UHMWPE wear debris from artificial joints has been described. Wear particles were extracted and isolated from periprosthetic tissues collected during revision surgery, of which was revised for loosening. The implant life of the patient was 12 years. The particles were examined by scanning electron microscopy. Digitized particle images were analyzed on a computer by specially developed software 'Image-Pro Plus'. The following nineteen numerical descriptors were used to characterize the particles: particle area, length, width, perimeter, boundary fractal dimension and shape parameters such as form factor, roundness, convexity, aspect ratio et al. Principal component analysis algorithm was applied to reduced the amount of the parameters in order to simplify the following calculation. Furthermore, main factors and important parameters such as mean diameter, ECD and perimeter were found out. However, C-means clustering algorithm was applied to classify the UHMWPE wear debris into four to seven clusters. Xie-Beni index was introduced to determine the optimal number of cluster and illuminate the clustering validity. The result of the calculation indicates that five clusters is the optimal clustering number. The feature of the debris in each cluster was also described in this paper.