<title>Automatic detection of lung nodules from multislice low-dose CT images</title>

Computer-assisted detection of pulmonary nodules: evaluation of diagnostic performance using an expert knowledge-based detection system with variable reconstruction slice thickness settings

“…Due to the inherently high contrast between soft tissues and lung tissues, it represents a particularly promising tool for optimized automated detection of pulmonary nodules from MSCT datasets. Several CAD approaches are currently undergoing clinical evaluation with preliminary evidence that CAD may be suited to guide the radiologist to suspicious lesions [20,[25][26][27][28][29][30][31]. Mathematical models for computer-aided detection of pulmonary nodules can be broadly divided into two categories: density-based approaches using the high density interval between the nodule and the pulmonary parenchyma employ techniques such as multiple thresholding [7,25,26], region-growing [20], locally adaptive thresholding in combination with region-growing [27] and fuzzy clustering [28] for nodule identification.…”

Computer-assisted detection of pulmonary nodules: performance evaluation of an expert knowledge-based detection system in consensus reading with experienced and inexperienced chest radiologists

“…Various CAD tools using the inherently high contrast between the nodular and lung tissues as platform for nodule recognition within density-based or model-based algorithms have been developed for analysis of thin section CT datasets [1,5,[12][13][14][15]38]. However, the currently published data is scarce, with only few studies having been performed to apply CAD to more than 50 nodules.…”

“…Consensus of reader 1+CAD significantly outperformed all other readings, demonstrating a benefit in using CAD as an inexperienced reader replacement. It is questionable whether inexperienced readers can be regarded as adequate for interpretation of pulmonary nodules in consensus with CAD, replacing an experienced radiologist.Computer-assisted detection of pulmonary nodules: performance evaluation of an expert knowledge-based detection system in consensus reading with experienced and inexperienced chest radiologists mance and may enhance the detection of suspicious lesions [1,[10][11][12][13][14][15][16]. However, CAD systems for automated lung nodule detection employing various density-based or model-based recognition algorithms have been predominantly tested on small numbers of artificial or in vivo pulmonary nodules without standardized databases that impede the comparison of detection performances between different authors.…”

Computer-aided detection and automated CT volumetry of pulmonary nodules

“…Various approaches have been developed for automated detection of pulmonary nodules at chest CT that from a technical point of view can be broadly divided into two major categories: density-based and model-based systems [8,[20][21][22][23][24]. Density-based approaches use the high-density interval between the nodule and the pulmonary parenchyma and employ techniques such as multiple thresholding [8,25], region-growing [20], locally adaptive Fig.…”