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Proceedings Paper

Probabilistic nodule filtering in thoracic CT scans
Author(s): Changhua Wu; Gady Agam
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Paper Abstract

Automated detection of lung nodules in thoracic CT scans is an important clinical challenge. Blood vessels form a major source of false positives in automated nodule detection systems. Hence, the performance of such systems may be improved by enhancing nodules while suppressing blood vessels. Ideally, nodule enhancement filters should enhance nodules while suppressing vessels and lung tissue. A distinction between vessels and nodules is normally obtained through eigenvalue analysis of the Hessian matrix. The Hessian matrix is a second order differential quantity and so is sensitive to noise. Furthermore, by relying on principal curvatures alone, existing filters are incapable of distinguishing between nodules and vessel junctions, and are incapable of handling cases in which nodules touch vessels. In this paper we develop novel nodule enhancement filters that are capable of suppressing junctions and are capable of handling cases in which nodules appear to touch or even overlap with vessels. The proposed filters are based on optimized probabilistic models derived from eigenvalue analysis of the gradient correlation matrix which is a first order differential quantity and so are less sensitive to noise compared with known vessel enhancement filters. The proposed filters are evaluated and compared to known techniques both qualitatively, quantitatively. The evaluation includes both synthetic and actual clinical data.

Paper Details

Date Published: 10 March 2006
PDF: 9 pages
Proc. SPIE 6144, Medical Imaging 2006: Image Processing, 61441T (10 March 2006); doi: 10.1117/12.654233
Show Author Affiliations
Changhua Wu, Kettering Univ. (United States)
Gady Agam, Illinois Institute of Technology (United States)

Published in SPIE Proceedings Vol. 6144:
Medical Imaging 2006: Image Processing
Joseph M. Reinhardt; Josien P. W. Pluim, Editor(s)

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