DRIVINGHIERARCHYCONSTRUCTIONVIASUPERVISEDLEARNING:APPLICATION TO OSTEO-ARTICULARMEDICALIMAGESDATABASE

Mostsimilarity ordissimilarity measures usedinmergingandsplitting segmentation methods include inalmost all cases asingle radiometrical information, integrate rarely geometrical information andignore thehighlevel knowledge on theimage. Consequently, theregion hierarchies issued from these approaches maysuffer fromastructural instability and deficiency inthesemantic oftheregions duetotheimage content, its highvariability andthecomplexity ofthemeaningful regions whichcompose this image. Inthis paper, wepropose toenhance the"semantic" content ofthehierarchy bymeans ofanadditional termcalled "contextual cost". Thistermintegrates thehighlevel knowledge ontheimagewhich isderived fromaclassifier after asupervised learning onthesemantic classes composing theimage. Itspurpose istobetter guide themerging process towards theconstruction ofmeaningful regions.