An automated graphical probabilistic framework for the detection of lung tumors in thoracic CT scan images

The subject of this thesis is the development of a automated graphical probabilistic framework to segment lung tumors from thoracic CT scan images. The feature space that we use to represent the images consists mostly of the responses of the initial images to convolution kernels of a Gabor lter...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριος συγγραφέας: Δαμόπουλος, Δημήτριος
Άλλοι συγγραφείς: Νικήτα, Κωνσταντίνα
Μορφή: Thesis
Γλώσσα:English
Έκδοση: 2017
Θέματα:
Διαθέσιμο Online:http://hdl.handle.net/10889/10767
Περιγραφή
Περίληψη:The subject of this thesis is the development of a automated graphical probabilistic framework to segment lung tumors from thoracic CT scan images. The feature space that we use to represent the images consists mostly of the responses of the initial images to convolution kernels of a Gabor lter bank. We are then constructing an AdaBoost strong classi er over a collection of simple decision trees. The output of our strong classi er is used to construct the unary potentials of a Markov Random Field (MRF) model, that improves our results by enforcing structure.