A computer-aided detection (CAD) system for the selection of lung nodules in computer tomography (CT) images is presented. The system is based on region growing (RG) algorithms and a new active contour model (ACM), implementing a local convex hull, able to draw the correct contour of the lung parenchyma and to include the pleural nodules. The CAD consists of three steps: (1) the lung parenchymal volume is segmented by means of a RG algorithm; the pleural nodules are included through the new ACM technique; (2) a RG algorithm is iteratively applied to the previously segmented volume in order to detect the candidate nodules; (3) a double-threshold cut and a neural network are applied to reduce the false positives (FPs). After having set the parameters on a clinical CT, the system works on whole scans, without the need for any manual selection. The CT database was recorded at the Pisa center of the ITALUNG-CT trial, the first Italian randomized controlled trial for the screening of the lung cancer. The detection rate of the system is 88.5% with 6.6 FPs/CT on 15 CT scans (about 4700 sectional images) with 26 nodules: 15 internal and 11 pleural. A reduction to 2.47 FPs/CT is achieved at 80% efficiency.

The purpose of this study is to develop a software for the extraction of the hippocampus and surrounding medial temporal lobe ͑MTL͒ regions from T1-weighted magnetic resonance ͑MR͒ images with no interactive input from the user, to introduce a novel statistical indicator, computed on the intensities in the automatically extracted MTL regions, which measures atrophy, and to evaluate the accuracy of the newly developed intensity-based measure of MTL atrophy to ͑a͒ distinguish between patients with Alzheimer disease ͑AD͒, patients with amnestic mild cognitive impairment ͑aMCI͒, and elderly controls by using established criteria for patients with AD and aMCI as the reference standard and ͑b͒ infer about the clinical outcome of aMCI patients. For the development of the software, the study included 61 patients with mild AD ͑17 men, 44 women; mean ageϮ standard deviation ͑SD͒, 75.8 yearsϮ 7.8; Mini Mental State Examination ͑MMSE͒ score, 24.1Ϯ 3.1͒, 42 patients with aMCI ͑11 men, 31 women; mean ageϮ SD, 75.2 yearsϮ 4.9; MMSE score, 27.9Ϯ 1.9͒, and 30 elderly healthy controls ͑10 men, 20 women; mean ageϮ SD, 74.7 yearsϮ 5.2; MMSE score, 29.1Ϯ 0.8͒. For the evaluation of the statistical indicator, 150 patients with mild AD ͑62 men, 88 women; mean ageϮ SD, 76.3 yearsϮ 5.8; MMSE score, 23.2Ϯ 4.1͒, 247 patients with aMCI ͑143 men, 104 women; mean ageϮ SD, 75.3 yearsϮ 6.7; MMSE score, 27.0Ϯ 1.8͒, and 135 elderly healthy controls ͑61 men, 74 women; mean ageϮ SD, 76.4 yearsϮ 6.1͒. Fifty aMCI patients were evaluated every 6 months over a 3 year period to assess conversion to AD. For each participant, two subimages of the MTL regions were automatically extracted from T1-weighted MR images with high spatial resolution. An intensity-based MTL atrophy measure was found to separate control, MCI, and AD cohorts. Group differences were assessed by using two-sample t test. Individual classification was analyzed by using receiver operating characteristic ͑ROC͒ curves. Compared to controls, significant differences in the intensitybased MTL atrophy measure were detected in both groups of patients ͑AD vs controls, 0.28Ϯ 0.03 vs 0.34Ϯ 0.03, P Ͻ 0.001; aMCI vs controls, 0.31Ϯ 0.03 vs 0.34Ϯ 0.03, P Ͻ 0.001͒. Moreover, the subgroup of aMCI converters was significantly different from controls ͑0.27Ϯ 0.034 vs 0.34Ϯ 0.03, P Ͻ 0.001͒. Regarding the ROC curve for intergroup discrimination, the area under the curve was 0.863 for AD patients vs controls, 0.746 for all aMCI patients vs controls, and 0.880 for aMCI converters vs controls. With specificity set at 85%, the sensitivity was 74% for AD vs controls, 45% for aMCI vs controls, and 83% for aMCI converters vs controls. The automated analysis of MTL atrophy in the segmented volume is applied to the early assessment of AD, leading to the discrimination of aMCI converters with an average 3 year follow-up. This procedure can provide additional useful information in the early diagnosis of AD.

The MAGIC-5 Project aims at developing Computer Aided Detection (CAD) software for Medical Applications on distributed databases by means of a GRID Infrastructure Connection. The use of automatic systems for analyzing medical images is of paramount importance in the screening programs, due to the huge amount of data to check. Examples are: mammographies for breast cancer detection, Computed-Tomography (CT) images for lung cancer analysis, and the Positron Emission Tomography (PET) imaging for the early diagnosis of the Alzheimer disease. The need for acquiring and analyzing data stored in different locations requires a GRID approach of distributed computing system and associated data management. The GRID technologies allow remote image analysis and interactive online diagnosis, with a relevant reduction of the delays actually associated to the screening programs. From this point of view, the MAGIC-5 collaboration can be seen as a group of distributed users sharing their resources for implementing different Virtual Organizations (VO), each one aiming at developing screening programs, tele-training, tele-diagnosis and epidemiologic studies for a particular pathology.

Typical patterns of hand-joint covariation arising in the context of grasping actions enable one to provide simplified descriptions of these actions in terms of small sets of hand-joint parameters. The computational model of mirror mechanisms introduced here hypothesizes that mirror neurons are crucially involved in coding and making this simplified motor information available for both action recognition and control processes. In particular, grasping action recognition processes are modeled in terms of a visuo-motor loop enabling one to make iterated use of mirror-coded motor information. In simulation experiments concerning the classification of reach-to-grasp actions, mirror-coded information was found to simplify the processing of visual inputs and to improve action recognition results with respect to recognition procedures that are solely based on visual processing. The visuo-motor loop involved in action recognition is a distinctive feature of this model which is coherent with the direct matching hypothesis. Moreover, the visuo-motor loop sets the model introduced here apart from those computational models that identify mirror neuron activity in action observation with the final outcome of computational processes unidirectionally flowing from sensory (and usually visual) to motor systems.

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