Semi-automatic segmentation of medical imagery

This disscrtation irnproves on the practical applaabihly of existing semiand fully automatic segmentationtechniqv.es by cwjniribv.ting the following two innovatwns: Firstly, it presenis a. new semi-automaticsegmentation sirategy that alleutales the inieraeiion diffieuities frequenÜy arising in the conlext of medical image segmenta¬ tion. And secondly. it introduces a new rrwded-based 'Initialisation and inieraeiion technique tlv.it extends the capab-üüies of Statistical modehbased segmentation ap¬ proach es. The segmentationof medical imagery is a diflicu.lt problem that cannot. at least to the present clay, be solved generically in a fully automaticmanner. As a consequence thereoh every segmentationtool should provide a suitable intcraction metaplior, so as to be practically applicable. Considering this requirement. we extend and moelify existing segmentationstrategies to improve tlieir inferface with the human Opera¬ tor. The presented Frameworks thereby paxtially bridge the functional gaps between rnan.ua], semi-automatic, and fully automaticsegmentationprocedures. Taken Over¬ all, the main contr.i.butio.ns of this clocforal work are (wofohl and primarily affect the fields of physically-baseddcformable modeis and Statistical modehbased segmentation approaches. In tlie context of the former subjeet area, we suggest two new ffarneworks that improve on the interaction metapliors oi classical physically-baseddeformablernoch eis. Practitioners offen coniplam about their insuffi.ci.ent controllability and the cumbersomestecring mechanisms that are available during automatic optimisation. Obviousiy, these methodstend to overlook the problems caused by missing or in.complefe image information. To eiiminate these inaclequacies. a truly semi-automatic segmentation Framework shouid comprise both a fine-grained intcraction concept and a powcrful mampulatiou semantic. While the Former can be achieved by a closer Integrationof the human Operator into the optimisation loop, the latter primarily clepends on the underlying geometiic representationof the objeet to be segmented. Accordingly, we presenta new approach thatis based on the combination of a powerful hierarchical modelling metaplior with traditional edge locaIi.sati.on mech¬ anisms. More preciselv, we suggest the usage of a particle system in 2-D, and the exploitafionof the thin plate under tension model being subjeet to a suitable set of