Ricerc@Sapienza

Computer graphics and signal processing have been jointly developed in the last decades. From one side, several tools from the signal processing can be directly extended on non-Euclidean domain thanks to the spectral geometry processing. From the other side, new devices and techniques for the acquisition of signals provide a huge quantity of information defined on complex non-Euclidean geometries.
The increased opportunity to precisely scan and acquire the geometry of real 3D objects gives rise to several new solutions in different applications such as fabrication and quality control. One application, with a huge impact in real life, is anomaly detection and analysis in the medical scenario. Our proposal aims to exploit the spectral geometry processing tools for this precise goal.
Recently, in medical centres and clinics, a huge diffusion of devices for 3D acquisition gave rise to a dramatical enhancement of the availability of digital representations of anatomical shapes, such as the external surfaces of organs, muscles, tissues and bones that compose the human body.
The geometry of these objects already contains a lot of information and is interesting by itself, some deformations of the surface of specific organs are indeed correlated with the occurrence of a disease. At the same time, these surfaces are usually enriched with additional numerical evaluations that give rise to a collection of signals defined on the surface such as thickness, curvatures and other clinical annotations.
The main idea of our project is to analyze signals defined on the considered surface through standard tools from signal processing in order to detect and classify the anomalies.
We believe that in the medical context, and in particular for organs analysis and disease detection, signal processing on the non-Euclidean domain can give rise to innovative and useful techniques and methods.