Medical image analysis
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Medical image analysis · Jun 2010
A new computationally efficient CAD system for pulmonary nodule detection in CT imagery.
Early detection of lung nodules is extremely important for the diagnosis and clinical management of lung cancer. In this paper, a novel computer aided detection (CAD) system for the detection of pulmonary nodules in thoracic computed tomography (CT) imagery is presented. The paper describes the architecture of the CAD system and assesses its performance on a publicly available database to serve as a benchmark for future research efforts. ⋯ The mean overlap between the nodule regions delineated by three or more radiologists and the ones segmented by the proposed segmentation algorithm is approximately 63%. Overall, with a specificity of 3 false positives (FPs) per case/patient on average, the CAD system is able to correctly identify 80.4% of the nodules (115/143) using 40 selected features. A 7-fold cross-validation performance analysis using the LIDC database only shows CAD sensitivity of 82.66% with an average of 3 FPs per CT scan/case.
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Medical image analysis · Jun 2010
Evaluation of brain atrophy estimation algorithms using simulated ground-truth data.
A number of analysis tools have been developed for the estimation of brain atrophy using MRI. Since brain atrophy is being increasingly used as a marker of disease progression in many neuro-degenerative diseases such as Multiple Sclerosis and Alzheimer's disease, the validation of these tools is an important task. However, this is complex, in the real scenario, due to the absence of gold standards for comparison. ⋯ Using these gold standards, an evaluation of the performance of three standard brain atrophy estimation methods (SIENA, SIENAX and BSI-UCD), on the basis of their robustness to various sources of error (bias-field inhomogeneity, noise, geometrical distortions, interpolation artefacts and presence of lesions), is presented. Our evaluation shows that, in general, bias-field inhomogeneity and noise lead to larger errors in the estimated atrophy than geometrical distortions and interpolation artefacts. Experiments on 18 different anatomical models of the brain after simulating whole brain atrophies in the range of 0.2-1.5% indicate that, in the presence of bias-field inhomogeneity and noise, a mean error of 0.64+/-0.53%,4.00+/-2.41% and 1.79+/-0.97% may be expected in the atrophy estimated by SIENA, SIENAX and BSI-UCD, respectively.