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Automatic detection of lung nodules is an important problem in computer analysis of chest radiographs. In this paper we propose a novel algorithm for isolating lung nodules from spiral CT scans. The proposed algorithm is based on using four different types of deformable templates describing typical geometry and gray level distribution of lung nodules. These four types are (i) solid spherical model of large-size calcified and non-calcified nodules appearing in several successive slices; (ii) hollow spherical model of large lung cavity nodules; (iii) circular model of small nodules appearing in only a single slice; and (iv) semicircular model of lung wall nodules. Each template has a specific gray level pattern which is analytically estimated in order to fit the available empirical data. The detection combines the normalized cross-correlation template matching by genetic optimization and Bayesian post-classification. This approach allows for isolating abnormalities which spread over several adjacent CT slices. Experiments with 200 patients’ CT scans show that the developed techniques detect lung nodules more accurately than other known algorithms.

Automatic Detection and Recognition of Lung Abnormalities in Helical CT Images Using Deformable Templates