Translator Disclaimer
27 March 2009 Enhanced detection in CT colonography using adaptive diffusion filtering
Author Affiliations +
Proceedings Volume 7259, Medical Imaging 2009: Image Processing; 725923 (2009) https://doi.org/10.1117/12.811563
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
Computer-aided detection (CAD) is a computerized procedure in medical science that supports the medical team's interpretations and decisions. CAD often uses information from a medical imaging modality such as Computed Tomography to detect suspicious lesions. Algorithms to detect these lesions are based on geometric models which can describe the local structures and thus provide potential region candidates. Geometrical descriptive models are very dependent on the data quality which may affect the false positive rates in CAD. In this paper we propose an efficient adaptive diffusion technique that adaptively controls the diffusion flux of the local structures in the data using robust statistics. The proposed method acts isotropically in the homogeneous regions and anisotropically in the vicinity of jump discontinuities. This method structurally enhances the data and makes the geometrical descriptive models robust. For the iterative solver, we use an efficient gradient descent flow solver based on a PDE formulation of the problem. The whole proposed strategy, which makes use of adaptive diffusion filter coupled with gradient descent flows has been developed and evaluated on clinical data in the application to colonic polyp detection in Computed Tomography Colonography.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Abdel Douiri, Musib Siddique, Xujiong Ye, Gareth Beddoe, and Greg Slabaugh "Enhanced detection in CT colonography using adaptive diffusion filtering", Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 725923 (27 March 2009); https://doi.org/10.1117/12.811563
PROCEEDINGS
10 PAGES


SHARE
Advertisement
Advertisement
Back to Top