Multi-resolution Fuzzy Clustering Approach for Image-Based Particle Characterization

This paper presents a novel technique based on combing wavelet transform and Fuzzy C-means Clustering (FCM) for particle image analysis. Through performing wavelet transform on images, the noise and high frequency components of images can be eliminated and the textures and features can be obtained. FCM is then used to divide data into two clusters to separate touching objects. To quantitatively evaluate this method, a case study involving a particle image is investigated. The procedure of selecting optimum wavelet function and decomposition level for this image is presented. 'Fuzzy range' is used as a derived feature for segmentation. The amounts of particles, particle equivalent diameters, and size distribution before and after partition are discussed. The results show that this method is effective and reliable.