| Data: |
2014 |
| Resum: |
This PhD dissertation is focused on the development of algorithms for the automatic segmentation of anatomical structures in biomedical images, usually the hippocampus in histological images from the mouse brain. Such algorithms are based on computer vision techniques and artificial intelligence methods. More precisely, on the one hand, we take advantage of deformable models to segment the anatomical structure under consideration, using prior knowledge from different sources, and to embed the segmentation into an optimization framework. On the other hand, metaheuristics and classifiers can be used to perform the optimization of the target function defined by the shape model (as well as to automatically tune the system parameters), and to refine the results obtained by the segmentation process, respectively. Three new different methods, with their corresponding advantages and disadvantages, are described and tested. A broad theoretical discussion, together with an extensive introduction to the state of the art, has also been included to provide an overview necessary for understanding the developed methods. |
| Nota: |
Advisor: Stefano Cagnoni. Date and location of PhD thesis defense: 10 March 2014, University of Parma |
| Drets: |
Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.  |
| Llengua: |
Anglès |
| Document: |
Altres ; recerca ; Versió publicada |
| Matèria: |
Biomedical imaging ;
Soft computing ;
Metaheuristics ;
Deformable models ;
Ensemble classifiers ;
Level set method ;
Active shape models ;
Hippocampus ;
Differential evolution ;
Multimodal optimization |
| Publicat a: |
ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 13, Núm. 2 (2014) , p. 24-25, ISSN 1577-5097 |
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