Unsupervised spatio-temporal filtering of image sequences. A mean-shift specification

• We propose an unsupervised spatio-temporal image filtering method based on mean-shift.
• We adapt the spatial and feature range domains to handle temporal evolution.
• A constraint is added on the sample’s evolution to select temporal neighbors.
• Our method outperforms standard mean-shift by adequately considering time information.
• Effectiveness is demonstrated on both synthetic and real brain MRI time-series data.

A new spatio-temporal filtering scheme based on the mean-shift procedure, which computes unsupervised spatio-temporal filtering for univariate feature evolution, is proposed in this paper. Our main contributions are on one hand the modification of the spatial/range domains to appropriately integrate the temporal feature into the mean-shift iterative form and on the other hand the addition of a trajectory constraint in the feature space with the use of the infinity norm. Therefore, only the samples living the same life in the feature space will converge. Major assets of the standard mean-shift framework such as convergence and bandwidth parameters adjustment are preserved. In this paper, we study the relative importance of the bandwidth parameters and the efficiency of the proposed method is assessed on synthetic data and compared to the standard mean-shift framework for spatio-temporal data filtering. The obtained results have allowed us to undertake a first study on real data, which has led to encouraging results in identifying spatio-temporal evolution of multiple sclerosis lesions appearing on T2-weighted MR images.