|Fig. 1: Coronal cross-section of the human brain (T1-weighted MRI)||Fig. 2: Segmentation of the data in fig. 1|
Segmentation can be defined
as the identification of meaningful image components. It is a fundamental
task in image processing providing the basis for any kind of high-level image
analysis. In medical image processing, a wide range of applications is based on
segmentation, e.g. the volumetric analysis with respect to normal or
pathological organ development, temporal monitoring of size and growth in
pathological processes, or as a basis for the applicability of automatic image
fusion algorithms when combining the complementary information obtained by different
image acquisition modalities.
In our group, we focus on several segmentation problems that are of specific interest in the field of neuroradiology, such as high-precision volume measurement of anatomical tissue classes in normal subjects and patients with psychiatric disorders, or the temporal monitoring of focal lesions for therapy control in patients with Multiple Sclerosis.
Fig. 1 shows a T1-weighted MRI cross-section from multispectral MRI data of the brain in a normal subject. Fig. 2 presents a fully-automatic segmentation of the data with regard to structure classes Gray Matter (plotted red), White Matter (plotted green), and Cerebrospinal Fluid (plotted blue). This result has been obtained by a novel neural network algorithm (so-called Deformable Feature Map) developed by our group that provides adaptive plasticity in function approximation problems. It reduces a class of similar function approximation problems to the explicit supervised one-shot training of a single data set, which is followed by a subsequent similarity transformation based on a self-organized deformation of the underlying multidimensional probability distributions.
Last Update: March-20-2002