Driving Hierarchy Construction via Supervised Learning: Application to Osteo-Articular Medical Images Database

Most similarity or dissimilarity measures used in merging and splitting segmentation methods include in almost all cases a single radiometrical information, integrate rarely geometrical information and ignore the high level knowledge on the image. Consequently, the region hierarchies issued from these approaches may suffer from a structural instability and deficiency in the semantic of the regions due to the image content, its high variability and the complexity of the meaningful regions which compose this image. In this paper, we propose to enhance the "semantic" content of the hierarchy by means of an additional term called "contextual cost". This term integrates the high level knowledge on the image which is derived from a classifier after a supervised learning on the semantic classes composing the image. Its purpose is to better guide the merging process towards the construction of meaningful regions.