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An automated system was developed for lung nodule detection in lung low-dose computer tomography (CT) scans. The system exploits a thorax anatomical model in order to distinguish the 3D anatomical structures corresponding, in the order, to the chest wall, the trachea and the two lung lobes. Each anatomical structure is described in terms of characteristics like volume, X-ray attenuation, and position with respect to structures already recognised. Once the two pulmonary parenchymas have been isolated from the rest of the chest, nodules are looked for inside them. More precisely, the robust Fuzzy C-Means algorithm is applied to classify the lung area into two clusters: the former includes nodules and blood vessels, the latter consists of air. 3D regions of interest (ROIs) are then identified in the former cluster and features are extracted from the ROIs for fuzzy neural network-based recognition of nodules from vessels. Experiments on 20 clinical cases (including about 7,000 overall images) showed 100% and nearly 82% correct recognition rate for nodules (0.5-3 cm) and micro-nodules (3-5 mm), respectively, and an average of 1.4 false positives for image

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In this paper we propose a novel method for feature selection based on a modified fuzzy C-means algorithm with supervision (MFCMS). MFCMS adopts an appropriately modified version of the objective function used by the classic fuzzy C-means. We applied MFCMS to some real-world pattern classification benchmarks. To test the effectiveness of MFCMS as feature selector, we used the well-known k-nearest neighbor as learning algorithm. In our experiments we found that the classification performance using the set of features selected by MFCMS is better than that using all the original features. Furthermore, our approach proved to be less time-consuming than other feature selection methods