Joint blind source separation: Applications in medical image analysis

Abstract

Summary form only given. Blind source separation (BSS) is based on a simple generative model and hence minimizes the assumptions on the nature of data. It provides a promising alternative to the traditional model-based approaches in many applications where the underlying dynamics are hard to characterize. Independent component analysis (ICA), in particular, has been a popular BSS approach and an active area of research. By imposing the constraint of statistical independence on the underlying components, ICA recovers linearly mixed components subject to only a scaling and permutation ambiguity, and has been successfully applied to numerous problems in areas as diverse as biomedicine, communications, finance, geophysics, and remote sensing. Blind separation of multiple datasets simultaneously, i.e., joint BSS, is becoming increasingly important in most of these application areas, for example in medical image analysis where data from multiple subjects need to be analyzed for subject level or group inferences.