In this study, we propose a robust fuzzy clustering method tailored for cellwise outlier detection. Unlike conventional robust fuzzy clustering approaches, our model relaxes the assumption of spherical clusters while maintaining the eigenvalue ratio constraint of F-TCLUST and effectively handling contaminated cells in the data. The estimation process is carried out via an EM algorithm, incorporating an additional step for identifying outliers, followed by E- and M-steps that treat contaminated cells as missing data. Through simulations we demonstrate the effectiveness of our approach in scenarios with significant cellwise contamination.