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An automated system is developed for lung and mediastinum segmentation in lung CT (Computed Tomography) images for the purpose of using these segmentations not only in CT images but also in PET (Positron Emission Tomography) images to exploit the useful integration of the CT and PET images performed by the highly valuable oncological equipment PET/CT. Segmentation is the most crucial step in a CAD...
We have developed a multi-classifier system for automatic classification of pulmonary nodules in lung CT (Computed Tomography) images. The system consists of a set of independent modules, each emulating a radiologist of a team, and a further module aimed at appropriately combining theradiologists' opinions. In the experiments we obtained a sensitivity of 95% against a specificity of 91.33%, adopting...
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...
In this paper, we describe a computer-aided diagnosis (CAD) system for automated detection of pulmonary nodules in computed-tomography (CT) examinations. After a segmentation phase based on the robust fuzzy c-means (RFCM) algorithm proposed by Pham, the regions of interest (ROIs) undergo both 2D and 3D morphological analysis in order to distinguish between nodule and blood vessel sections. The system...
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