Connected image processing with multivariate attributes: An unsupervised Markovian classification approach

• We perform an unsupervised classification of the nodes of the Max-Tree.
• The Max-Tree is considered as a hidden Markov tree.
• Multivariate probability density functions enables to model multivariate attributes.
• Model parameters are estimated from the sole observations.
• We perform experiments on astronomical images and retinal images segmentation.

This article presents a new approach for constructing connected operators for image processing and analysis. It relies on a hierarchical Markovian unsupervised algorithm in order to classify the nodes of the traditional Max-Tree. This approach enables to naturally handle multivariate attributes in a robust non-local way. The technique is demonstrated on several image analysis tasks: filtering, segmentation, and source detection, on astronomical and biomedical images. The obtained results show that the method is competitive despite its general formulation. This article provides also a new insight in the field of hierarchical Markovian image processing showing that morphological trees can advantageously replace traditional quadtrees.