Gliomas are the most common primary brain tumors, with a typical infiltrative growth pattern along white matter (WM) fibers. Diffusion Tensor Imaging (DTI) is sensitive to the directional diffusion of water along WM tracts, which allows the identification of subtle peritumoral glioma infiltration that are not apparent on conventional Magnetic Resonance imaging. The aim of this study was to characterize pathological and healthy tissue in DTI datasets by statistical texture analysis, developing a Computer Assisted Detection (CAD) technique for cerebral glioma. This system, coupled to voxel-based tumor evolution analysis, could allow objective tumor identification and qualitative and quantitative measurements in the follow-up of patients during chemotherapy. In this paper, preliminary results of tumor segmentation and evolution analysis are shown. © 2010 IEEE.
Automatic segmentation and therapy follow-up of cerebral glioma in diffusion-tensor images / De Nunzio, G.; Donativi, M.; Pastore, G.; Bello, L.; Soffietti, R.; Falini, A.; Castellano, A.. - (2010), pp. 43-47. (Intervento presentato al convegno 8th IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2010 tenutosi a Taranto, ita nel 2010) [10.1109/CIMSA.2010.5611767].
Automatic segmentation and therapy follow-up of cerebral glioma in diffusion-tensor images
Falini A.;Castellano A.Ultimo
2010-01-01
Abstract
Gliomas are the most common primary brain tumors, with a typical infiltrative growth pattern along white matter (WM) fibers. Diffusion Tensor Imaging (DTI) is sensitive to the directional diffusion of water along WM tracts, which allows the identification of subtle peritumoral glioma infiltration that are not apparent on conventional Magnetic Resonance imaging. The aim of this study was to characterize pathological and healthy tissue in DTI datasets by statistical texture analysis, developing a Computer Assisted Detection (CAD) technique for cerebral glioma. This system, coupled to voxel-based tumor evolution analysis, could allow objective tumor identification and qualitative and quantitative measurements in the follow-up of patients during chemotherapy. In this paper, preliminary results of tumor segmentation and evolution analysis are shown. © 2010 IEEE.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
