We present an approach based on self organizing maps to segment renal arteries from 2D PC Cine MR images to measure blood velocity and flow. Such information are important in grading renal artery stenosis and support the decision on surgical interventions like percutan transluminal angioplasty. Results show that the renal arteries could be extracted automatically. The corresponding velocity pro...

In this article, we introduce the concept of pathways of wellbeing and examine how such paths can be discovered from large data sets using the self-organizing map. Data sets used in the illustrative experiments include measurements of physical fitness and subjective assessments related to diagnosing work stress.

State-of-the-art systems for automatic writer identification from handwritten text are based on two approaches: a statistical approach or a model-based approach. Both approaches have limitations. The main limitation of the statistical approach is that it relies on single-scale statistical features. The main limitation of the model-based approach is that the codebook generation is time-consuming...

Kohonen’s Self-Organizing Map is a popular method which allows the projection of high dimensional data onto a low dimensional display space. Models of Self-Organizing Maps for the treatment of graphs have also been defined and studied. This paper proposes an extension to the GraphSOM model which substantially improves the stability of the model, and, as a side effect, allows for an acceleration...

The basic SOM is indi erent to the ordering of the input patterns. Real data, however, is often sequential in nature thus context of a pattern may signi cantly in uence its correct interpretation. One simple SOM model that takes the context of a pattern into account is the Temporal K ohonen Map (TKM),which was modi ed into the Recurrent Self Organizing Map (RSOM). We sho w analytically and with...

A technique for segmentation of images of baked good is presented. The technique employs a Self-Organising Map to identify the characteristic colour development curve (bake curve) for each product. Segmentation is based upon the colour information contained in the bake curve. The technique is trained with only positive exemplars of the product.