Creating Graph Abstractions for the Interpretation of Combined Functional and Anatomical Medical Images

Abstract

The characteristics of the images produced by advanced scanning technologies has led to medical imaging playing a critical role in modern healthcare. The most advanced medical scanners combine different modalities to produce multi-dimensional (3D/4D) complex data that is time-consuming and challenging interpret. The assimilation of these data is further compounded when multiple such images have to be compared, e.g., when assessing a patient’s response to treatment or results from a clinical search engine. Abstract representations that present the important discriminating characteristics of the data have the potential to prioritise the critical information in images and provide a more intuitive overview of the data, thereby increasing productivity when interpreting multiple complex medical images. Such abstractions act as a preview of the overall information and allow humans to decide when detailed inspection is necessary. Graphs are a natural method for abstracting medical images as they can represent the relationships between any pathology and the anatomical structures they affect. In this paper, we present a scheme for creating abstract graph visualisations that facilitate an intuitive comparison of the anatomy-pathology relationships within complex medical images. The properties of our abstractions are derived from the characteristics of regions of interest (ROIs) within the images. We demonstrate how our scheme is used to preview, interpret, and compare the location of tumours within volumetric (3D) functional and anatomical images.