Robust centroid determination of noisy data using FCM and domain specific partitioning

Abstract

Functional magnetic resonance imaging (FMRI) datasets are composed of spatial and temporal features and contain unique noise degradation. We propose a feature partition along noise-specific domains to fit the fuzzy c-means (FCM) algorithm to this problem. Each domain will consist of unique features and use a domain-specific metric. The distance term in the FCM membership update equation is replaced by a weighted sum of domain distances. Exploiting conceptual cleavage of the sample features invites intuitive remedial action in the form of robust metrics, decreased weighting, or selective enhancement processing. Robust centroids are determined by suppressing the role of feature subsets contaminated by significant noise levels or intractable noise types. This paper examines synthetic datasets of FMRI activations and shows that a specialized FCM algorithm determines higher accuracy centroids in the presence of high noise levels.