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Predicting Outcomes of Nonsmall Cell Lung Cancer Using CT Image Features., , , , , , , , , and . IEEE Access, (2014)Improving malignancy prediction through feature selection informed by nodule size ranges in NLST., , , , , , and . SMC, page 1939-1944. IEEE, (2016)Clustering granulometric features., , , and . Image Processing: Algorithms and Systems, volume 4667 of SPIE Proceedings, page 36-42. SPIE, (2002)Granulometric classifiers from small samples., , , , and . Image Processing: Algorithms and Systems, volume 4667 of SPIE Proceedings, page 100-107. SPIE, (2002)Optimal linear granulometric estimation for random sets., and . Pattern Recognition, 35 (6): 1315-1325 (2002)Test-Retest Reproducibility Analysis of Lung CT Image Features., , , , , , , , , and 5 other author(s). J. Digital Imaging, 27 (6): 805-823 (2014)Quantitative imaging features to predict cancer status in lung nodules., , , , , , , , , and . Medical Imaging: Image Perception, Observer Performance, and Technology Assessment, volume 9787 of SPIE Proceedings, page 97870L. SPIE, (2016)A Constant-time Algorithm for Erosions/Dilations with Applications to Morphological Texture Feature Computation., , , , and . Real-Time Imaging, 6 (3): 223-239 (2000)Granulometric parametric estimation for the random Boolean model using optimal linear filters and optimal structuring elements., and . Pattern Recognition Letters, 24 (1-3): 283-293 (2003)Asymptotic joint normality of the granulometric moments., , and . Pattern Recognition Letters, 22 (14): 1537-1543 (2001)