Using complex networks towards information retrieval and diagnostics in multidimensional imaging

TL;DR

This paper introduces a novel approach using complex network theory to analyze multidimensional dynamic images for improved information retrieval and diagnostics, demonstrated on thermal imaging data for eye diseases and surgeries.

Contribution

The paper presents a new framework transforming multidimensional images into networks, enabling effective diagnostics and analysis of fluctuations in medical imaging data.

Findings

Network analysis of thermal imaging distinguishes disease states.

Method shows similarity across different eye conditions and treatments.

Framework is scalable to other fluctuation-based diagnostic devices.

Abstract

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.