Detection and recognition of lung abnormalities using deformable templates
作者： A. El-Baz , G. Gimel'farb , R. Falk , A.A. Farag
关键词:
摘要: Automatic detection and recognition of lung cancer during mass screening spiral computer tomographic (CT) chest scans is one the most important problems today's medical image analysis. We propose an algorithm for isolating abnormalities (nodules) from arteries, veins, bronchi, bronchioles after all these objects have been already separated surrounding anatomical structures. The separation presented elsewhere, this paper focuses on nodule using deformable 3D 2D templates describing typical geometry gray level distribution within nodules same type. combines normalized cross-correlation template matching by genetic optimization Bayesian post-classification. Experiments with 200 low dose CT (LDCT) confirm accuracy our approach.
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