Lung nodule diagnosis using 3D template matching
Computers in Biology and Medicine
MIRAGE'11 Proceedings of the 5th international conference on Computer vision/computer graphics collaboration techniques
A fast algorithm for template matching
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
Automatic lung nodule detection using template matching
ADVIS'06 Proceedings of the 4th international conference on Advances in Information Systems
Computers in Biology and Medicine
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Automatic detection and recognition of lung cancer during mass screening of spiral computer tomographic (CT) chest scans is one of most important problems of todays medical image analysis. We propose an algorithm for isolating lung abnormalities (nodules) from arteries, veins, bronchi, and bronchioles after all these objects have been already separated from the surrounding anatomical structures. The separation is presented elsewhere, and this paper focuses on nodule detection using deformable 3D and 2D templates describing typical geometry and gray level distribution within the nodules of the same type. The detection combines normalized cross-correlation template matching by genetic optimization and Bayesian post-classification. Experiments with 200 spiral low dose CT (LDCT) scans confirm the accuracy of our approach.