A method of feature extraction and smoothing of a noisy contour of a particle is developed using a Bayesian model. The time series representing boundary points of a contour is able to be decomposed into trend, periodical, autoregressive and observation error components by the fixed-interval smoother algorithm with the Kalman filter. The performance of this smoothing algorithm is illusrated with some examples.