Lung nodule detection via Bayesian voxel labeling

Authors:
Paulo R. S. Mendonça;Rahul Bhotika;Fei Zhao;James V. Miller
Affiliations:
GE Global Research, One Research Circle, Niskayuna, NY;GE Global Research, One Research Circle, Niskayuna, NY;University of Iowa, Department of Electrical Engineering, Iowa City, IA;GE Global Research, One Research Circle, Niskayuna, NY
Venue:
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Year:
2007

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Abstract

This paper describes a system for detecting pulmonary nodules in CT images. It aims to label individual image voxels in accordance to one of a number of anatomical (pulmonary vessels or junctions), pathological (nodules), or spurious (noise) events. The approach is orthodoxly Bayesian, with particular care taken in the objective establishment of prior probabilities and the incorporation of relevant medical knowledge. We provide, under explicit modeling assumptions, closed-form expressions for all the probability distributions involved. The technique is applied to real data, and we present a discussion of its performance.