Diagnosis of lung nodule using reinforcement learning and geometric measures
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
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Shape characterization of small pulmonary nodules plays a significant role in differential diagnosis that discriminates malignant and benign nodules at early stages of pulmonary lesion development. This paper presents a method to characterize small pulmonary nodules based on the morphology of the development of lung lesions in thin-section CT images. The feature extraction algorithms are designed to extract the shape characteristic parameters from three-dimensional (3-D) nodule images using surface curvatures and ridge line. Experiments which show the feasibility of our method to improve the diagnostic accuracy are also demonstrated by applying the method to nodule images.