Graph and Point Cloud Matching for Image Registration
Typ dokumentu
disertační práceAutor
Pinheiro, Miguel Amável
Vedoucí práce
Kybic, Jan
Studijní obor
Umělá inteligence a biokybernetikaStudijní program
Elektrotechnika a informatikaInstituce přidělující hodnost
České vysoké učení technické v Praze. Fakulta elektrotechnická. Katedra kybernetiky.Metadata
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This thesis focuses on the topic of image or volume registration of data containing tree
and graph shaped structures, with a special focus on medical imaging. The geometrical
information is first extracted from the volumes or images and then used for registration.
We propose a method for the segmentation of trees in images acquired at different time
instances, by enforcing time consistency. This results in an overall improvement of the
extraction accuracy. The method was tested on medical, biological and road images.
The focus of this thesis is finding the alignment between segmented graphs and
trees. We first propose a method called Active Testing Search (ATS) that explores
partial correspondences of branching points of the structures. The method estimates the
probability of partial match correctness based on training data and incrementally grows
these partial matches. The ATS approach was able to align real data from several different
medical imaging modalities, and is robust to initial position, rotation, deformation, missing
data and noise.
The second proposed method is called Graph Matching using Monte Carlo tree search
(GMMC). The approach uses a stochastic state-space search algorithm inspired by the
Monte Carlo tree search method to build a large set of compatible curves. Further
acceleration is achieved by pruning using novel curve descriptors. The method can handle
partial matches, topological differences, geometrical distortion, does not use appearance
information and foes not require an initial alignment. Moreover, our method is very
efficient – it can match graphs with thousands of nodes, which is an order of magnitude
better than the best competing method.
Kolekce
- Disertační práce - 13000 [740]
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