Partition-induced connections and operators for pattern analysis

In this paper we present a generalization on the notion of image connectivity similar to that modeled by second-generation connections. The connected operators based on this new type of connection make use of image partitions aided by mask images to extract path-wise connected regions that were previously treated as sets of singletons. This leads to a redistribution of image power which affects texture descriptors. These operators find applications in problems involving contraction-based connectivities, and we show how they can be used to counter the over-segmentation problem of connected filters. Despite restrictions which prevent extensions to gray-scale, we present a method for gray-scale spectral analysis of biomedical images characterized by filamentous details. Using connected pattern spectra as feature vectors to train a classifier we show that the new operators outperform the existing contraction-based ones and that the classification performance competes with, and in some cases outperforms methods based on the standard 4- or 8-connectivity. Finally, combining the two methods we enrich the texture description and increase the overall classification rate.