Annual Report 2010 - Fachgruppe Informatik an der RWTH Aachen ...

Continuous Representation of Digital Images
Colas Schretter, Leif Kobbelt
The aim of this research project is to explore a new way to represent continuous digital
images in general. In particular, the paradigm of continuous image representation is totally
new for medical imaging and contrasts with established discrete image models based on
histograms. With a sparse and continuous model, the image space is not limited by sharp
boundaries and the number of image elements, hence the resolution, can be adapted locally as
a function of the amount of acquired input information for image reconstruction.
The competition toward higher-resolution medical imaging is an exiting driving application.
The size of medical images tends to grow dramatically (cubic law) with the increasing
potential resolution of medical scanners. Furthermore, traditional image processing tasks such
as image transformation, low- and high-pass filtering, registration and segmentation could be
solved more naturally on alternative sparse image models instead of being limited by hard
constraints associated to the classical regular grids of identical image elements.
(a) 100K random samples (b) Continuous representation (c) Ground truth image
From a stream of 100K random point sample data (a), a robust online statistical method
estimates an adaptive resolution continuous image representation of 128 multivariate
Gaussian components (b) that approximates the ground truth image (c). This experiment was
conducted on a brain slice acquired in a positron emission tomography (PET) scanner.
289