Title :
Modeling tumor growth: from differential deformable models to growth prediction of tumors detected in PET images
Author :
Garbey, M. ; Zouridakis, G.
Author_Institution :
Dept. of Comput. Sci., Houston Univ., TX, USA
Abstract :
Modeling of a growing tumor over time is extremely difficult, because of the complex biological phenomena underlying cancer proliferation. Existing models can mostly describe in vitro experiments of spherically-shaped avascular tumors, but they cannot match the highly heterogeneous and complex-shaped tumors seen in cancer patients. We propose a new time-dependent geometric deformable model that can characterize tumors of complex shape, such as vascular tumors. Preliminary result show that such a model can provide an adequate framework for analyzing and predicting the growth of tumors seen in PET images of cancer patients.
Keywords :
cancer; medical image processing; physiological models; positron emission tomography; tumours; PET images; cancer patients; cancer proliferation; complex biological phenomena; complex-shaped tumors; differential deformable models; growth prediction; highly heterogeneous tumors; spherically-shaped avascular tumors; time-dependent geometric deformable model; tumor detection; tumor growth modeling; Biological system modeling; Biomedical imaging; Cancer; Deformable models; Equations; Image analysis; Neoplasms; Positron emission tomography; Predictive models; Shape;
Conference_Titel :
Engineering in Medicine and Biology Society, 2003. Proceedings of the 25th Annual International Conference of the IEEE
Print_ISBN :
0-7803-7789-3
DOI :
10.1109/IEMBS.2003.1280470