Mathematical methods to predict the dynamic shape evolution of cancer growth based on spatio-temporal bayesian and geometrical models
- Autores: Iulian Teodor Vlad
- Directores de la Tesis: Jorge Mateu Mahiques (codir. tes.)
, José Joaquín Gual Arnau (codir. tes.) - Lectura: En la Universitat Jaume I ( España ) en 2016
- Idioma: inglés
- Número de páginas: 176
- Tribunal Calificador de la Tesis: Carlos Díaz Avalos (presid.) , Francico Javier Rodríguez Cortés (secret.) , Ciprian Crainiceanu (voc.)
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- Resumen
The aim of this research is to observe the dynamics of cancer tumors and to develop and implement new methods and algorithms for prediction of tumor growth. I offer some tools to help physicians for a better understanding this disease and to check if the prescribed treatment have the desired results.
The plan of the thesis is the following. In Chapter 1 I briefly recall some properties and classification of points processes with some examples of spatio-temporal point processes. Chapter 2 presents a short overview of the theory of Levy bases and integration with respect to such basis is given, I recall standard results about spatial Cox processes, and finally I propose different types of growth models and a new algorithm, the Cobweb, which is presented and developed based on the proposed methodology. Chapters 3, 4 and 5 are dedicated to present new prediction methods. The implementation in Matlab software comes in Chapter 6. The thesis ends with some conclusion and future research.
