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Preprocessing for Quantitative Statistical Noise Analysis of MDCT Brain Images Reconstructed Using Hybrid Iterative (iDose) Algorithm., , , , and . Journal of WSCG, 20 (1): 73-80 (2012)Registration and Fusion of the Autofluorescent and Infrared Retinal Images., , and . Int. J. Biomedical Imaging, (2008)Retinal image analysis aimed at blood vessel tree segmentation and early detection of neural-layer deterioration., , , and . Comput. Medical Imaging Graph., 36 (6): 431-441 (2012)Deep convolutional neural network-based segmentation and classification of difficult to define metastatic spinal lesions in 3D CT data., , , , , , , and . Medical Image Anal., (2018)Deep-learning Based Prediction of Virtual Non-contrast CT Images., , , , and . ISEEIE, page 72-76. ACM, (2021)Deconvolution of neuronal signal from hemodynamic response., , , and . ICASSP, page 617-620. IEEE, (2011)Analysis of Visual Appearance of Retinal Nerve Fibers in High Resolution Fundus Images: A Study on Normal Subjects., , , , , , , , and . Comput. Math. Methods Medicine, (2013)Thickness related textural properties of retinal nerve fiber layer in color fundus images., , , , , , , , , and 4 other author(s). Comput. Medical Imaging Graph., 38 (6): 508-516 (2014)Dynamic Granger causality based on Kalman filter for evaluation of functional network connectivity in fMRI data., , , and . NeuroImage, 53 (1): 65-77 (2010)Dynamic modeling of neuronal responses in fMRI using cubature Kalman filtering., , , , and . NeuroImage, 56 (4): 2109-2128 (2011)