A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification
By Keelin Murphy, B. van Ginneken, Arnold M. R. Schilham, B. Hoop, Hester A. Gietema, Mathias Prokop. Medical Image Anal., 2009
342 citations recorded by the source corpus.
Subfields: Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/1b28c67c8c23bb85dd86a56e661ece107dd4c98f
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In Pulmonary and Respiratory Medicine
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge (2016, methodology): “The generic nodule candidate detection algorithm was developed by Murphy et al. (2009). First, the image is downsampled from 512 × 512 to 256 × 256 with the”
- Multilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection (2017, background): “, intensity thresholding, shape curvedness, and mathematical morphology [3], [9], [10].”
- DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification (2018, methodology): “Traditional approaches generally require manually designed features such as morphological features, voxel clustering and pixel thresholding [20, 15].”