His primary areas of study are Artificial intelligence, Computer vision, Noise reduction, Pattern recognition and Non-local means. His research on Artificial intelligence focuses in particular on Segmentation. His Segmentation research includes elements of Image processing and Neuroimaging.
His Noise reduction research is multidisciplinary, relying on both Filter, Wavelet, Noise measurement, Principal component analysis and Gaussian noise. His Pattern recognition research integrates issues from Magnetic resonance imaging and Robustness. The study incorporates disciplines such as Image scaling and Peak signal-to-noise ratio in addition to Non-local means.
José V. Manjón mainly investigates Artificial intelligence, Pattern recognition, Segmentation, Computer vision and Noise reduction. Deep learning, Non-local means, Brain segmentation, Image processing and Robustness are the subjects of his Artificial intelligence studies. His research integrates issues of Grading, Image and Early detection in his study of Pattern recognition.
His work on Scale-space segmentation as part of general Segmentation study is frequently connected to Fusion, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. Many of his research projects under Computer vision are closely connected to Self-similarity with Self-similarity, tying the diverse disciplines of science together. His research investigates the connection between Noise reduction and topics such as Filter that intersect with problems in Image noise.
His primary areas of investigation include Artificial intelligence, Pattern recognition, Deep learning, Segmentation and Convolutional neural network. José V. Manjón is studying Image processing, which is a component of Artificial intelligence. His work carried out in the field of Pattern recognition brings together such families of science as Hippocampus and Cognition, Cognitive impairment.
His Segmentation study combines topics from a wide range of disciplines, such as Intracranial Cavity and Multi feature. His work deals with themes such as Normalization and Noise reduction, which intersect with Convolutional neural network. As a part of the same scientific family, José V. Manjón mostly works in the field of Brain segmentation, focusing on Robustness and, on occasion, Brain mri and Computer vision.
His scientific interests lie mostly in Deep learning, Artificial intelligence, Convolutional neural network, Pattern recognition and Hippocampal formation. His research in Deep learning intersects with topics in Segmentation and Brain segmentation. His studies in Brain segmentation integrate themes in fields like Transfer of learning, Ensemble learning and Machine learning.
His Pattern recognition research includes themes of Brain mri, Noise reduction and Robustness. His Hippocampal formation research is multidisciplinary, incorporating perspectives in Clinical syndrome, Disease, Pediatrics and Atrophy.
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Adaptive non‐local means denoising of MR images with spatially varying noise levels
José V. Manjón;Pierrick Coupé;Luis Martí-Bonmatí;D. Louis Collins.
Journal of Magnetic Resonance Imaging (2010)
Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation.
Pierrick Coupé;José V. Manjón;Vladimir S. Fonov;Jens C. Pruessner.
MRI denoising using non-local means.
José V. Manjón;José Carbonell-Caballero;Juan José Lull;Gracián García-Martí.
Medical Image Analysis (2008)
BEaST: brain extraction based on nonlocal segmentation technique.
Simon Fristed Eskildsen;Pierrick Coupé;Vladimir Fonov;José V. Manjón.
volBrain: An Online MRI Brain Volumetry System
José V. Manjón;Pierrick Coupé.
Frontiers in Neuroinformatics (2016)
Diffusion Weighted Image Denoising Using Overcomplete Local PCA
José V. Manjón;Pierrick Coupé;Luis Concha;Antonio Buades.
PLOS ONE (2013)
New methods for MRI denoising based on sparseness and self-similarity.
José V. Manjón;Pierrick Coupé;Antonio Buades;D. Louis Collins.
Medical Image Analysis (2012)
Robust Rician noise estimation for MR images.
Pierrick Coupé;Pierrick Coupé;José V. Manjón;Elias Gedamu;Elias Gedamu;Douglas L. Arnold;Douglas L. Arnold.
Medical Image Analysis (2010)
Non-local MRI upsampling.
José V. Manjón;Pierrick Coupé;Antonio Buades;Vladimir S. Fonov.
Medical Image Analysis (2010)
MRI noise estimation and denoising using non-local PCA
José V. Manjón;Pierrick Coupé;Antonio Buades.
Medical Image Analysis (2015)
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