Kernelized Fuzzy C-Means Method and Gaussian Mixture Model in Unsupervised Cascade Clustering

Fuzzy C-Means (FCM) clustering and Gaussian Mixture Model (GMM) are two popular tools for data processing. In this study the unsupervised algorithm combining FCM clustering in the Kernel Space (KFCM) and GMM is presented. First, a "kernel trick" is applied to the FCM algorithm. Then, the number of clusters is chosen automatically in the kernel space. On the basis of obtained starting parameters, i.e. number of mixture components, mean vector, covariance matrices and mixing proportion coefficients, the final GMM parameters are estimated. For this estimation the Expectation Maximization (EM) algorithm is used. The presented methodology - combination of KFCM and GMM methods named unsupervised cascade clustering - constitutes the basic step in Ewing's sarcoma segmentation. On this basis the voxels intensity values describing segmented tumour and surrounded healthy tissue are defined and fuzzy connectedness analysis is performed. The obtained mixture parameters estimation results are compared with the results obtained using two different methods described in literature.