Effects of including spatial information in clustering multivariate image data

Multivariate image data provide detailed information in variable and image space. Most traditional clustering methods are based on variable information only and ignore spatial information. A method based on both variable and spatial information could improve the results substantially.

In this review, we study the benefits and the pitfalls of including spatial information in chemometric clustering techniques. Spatial information is taken into account in initialization of clustering parameters, during cluster iterations by adjusting the similarity measure or at a post-processing step. We illustrate the effect of taking spatial information into account by a univariate synthetic data set and two real-world multivariate data sets. We show that methods that include neighboring pixel information in the clustering procedure improve the performance accuracy of the clustering in most cases. Homogeneous regions in the image are better recognized and the amount of noise is reduced by these methods.