  We present a fresh and broad yet simple approach towards information
retrieval in general and diagnostics in particular by applying the theory of
complex networks on multidimensional, dynamic images. We demonstrate a
successful use of our method with the time series generated from high content
thermal imaging videos of patients suffering from the aqueous deficient dry eye
(ADDE) disease. Remarkably, network analyses of thermal imaging time series of
contact lens users and patients upon whom Laser-Assisted in situ Keratomileusis
(Lasik) surgery has been conducted, exhibit pronounced similarity with results
obtained from ADDE patients. We also propose a general framework for the
transformation of multidimensional images to networks for futuristic biometry.
Our approach is general and scalable to other fluctuation-based devices where
network parameters derived from fluctuations, act as effective discriminators
and diagnostic markers.
