Lung nodule detection on thoracic computed tomography images: preliminary evaluation of a computer-aided diagnosis system.

By Metin Nafi Gurcan, Berkman Sahiner, N. Petrick, Heang-Ping Chan, E. Kazerooni, P. Cascade, Lubomir M. Hadjiiski. Medical Physics (Lancaster), 2002

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Shape-Based Computer-Aided Detection of Lung Nodules in Thoracic CT Images (2009, methodology): “Intensity-based detection methods are usually based on the assumption that lung nodules have relatively higher intensity than those of lung parenchyma and employ techniques such as multiple thresholding [3]; clustering [4]; artificial neural networks [5]; and mathematical morphology [6],”
Computer analysis of computed tomography scans of the lung: a survey (2006, methodology): “[121] used -means clustering on intensities in the original and median-filtered images.”
Current status and future potential of computer-aided diagnosis in medical imaging. (2005, background): “A number of investigators [50–71] have attempted to develop CAD schemes for computerised detection of lung nodules using various methods and techniques.”
Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification (2017, methodology): “Image-based techniques for analyzing lesions are normally performed with detection [7,8], segmentation [9 – 12], hand-crafted feature engineering [13,14], and category labelling [15 – 18].”