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Proceedings Paper

Enhanced detection in CT colonography using adaptive diffusion filtering
Author(s): Abdel Douiri; Musib Siddique; Xujiong Ye; Gareth Beddoe; Greg Slabaugh
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Paper 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.

Paper Details

Date Published: 27 March 2009
PDF: 10 pages
Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 725923 (27 March 2009); doi: 10.1117/12.811563
Show Author Affiliations
Abdel Douiri, Medicsight PLC (United Kingdom)
Musib Siddique, Medicsight PLC (United Kingdom)
Xujiong Ye, Medicsight PLC (United Kingdom)
Gareth Beddoe, Medicsight PLC (United Kingdom)
Greg Slabaugh, Medicsight PLC (United Kingdom)

Published in SPIE Proceedings Vol. 7259:
Medical Imaging 2009: Image Processing
Josien P. W. Pluim; Benoit M. Dawant, Editor(s)

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