Using complex networks towards information retrieval and diagnostics in multidimensional imaging
This provides a general and scalable framework for diagnostics in medical imaging, though it appears incremental as it applies existing network theory to new data types.
The paper tackles information retrieval and diagnostics by applying complex network theory to multidimensional dynamic images, demonstrating success with thermal imaging videos of dry eye disease patients and showing similarity in results for contact lens users and Lasik surgery patients.
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.