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...
| Κύριος συγγραφέας: | |
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| Άλλοι συγγραφείς: | |
| Μορφή: | Thesis |
| Γλώσσα: | English |
| Έκδοση: |
2017
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| Θέματα: | |
| Διαθέσιμο Online: | http://hdl.handle.net/10889/10767 |
| 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. |
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