Paper Info
Title
Probabilistic nodule filtering in thoracic CT scans
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.
Year
DOI
Venue
2006
10.1117/12.654233
Proceedings of SPIE
Keywords
Field
DocType
false positive,matrices,correlation matrix,first order,ct scan,probabilistic model
Biomedical engineering,Medical imaging,Computer science,First order,Computer-aided diagnosis,Hessian matrix,Filter (signal processing),Radiology,Covariance matrix,Probabilistic logic,False positive paradox
Conference
Volume
ISSN
Citations 
6144
0277-786X
2
PageRank 
References 
Authors
0.38
0
2
Name
Order
Citations
PageRank
Changhua Wu118916.89
Gady Agam239143.99