BACKGROUND Biomedical imaging research increasingly involves acquiring, managing and processing large amounts of distributed imaging data. Integrated systems that combine data, meta-data and workflows are crucial for realising the opportunities presented by advances in imaging facilities. METHODS This paper describes the design, implementation and operation of a multi-modality research imagin...

Biomedical imaging represents a practical and conceptual revolution in the applied sciences of the last thirty years. Two basic ingredients permitted such a breakthrough: the technological development of hardware for the collection of detailed information on the organ under investigation in a less and less invasive fashion; the formulation and application of sophisticated mathematical tools for...

Our purpose in this talk is to advocate the use of wavelets for advanced bioimaging. We start with a short tutorial on wavelet bases, emphasizing the fact that they provide a concise multiresolution representation of images and that they can be computed most efficiently. We then discuss a simple but remarkably effective image-denoising procedure that essentially amounts to discarding small wave...

Áurea Castilho, Eirik Madsen, António F. Ambrósio, Margaret L. Veruki, and Espen Hartveit Department of Biomedicine, University of Bergen, Bergen, Norway; Institute of Biomedical Imaging and Life Sciences (IBILI), Faculty of Medicine, University of Coimbra, Coimbra, Portugal; Center for Neuroscience and Cell Biology, Institute of Biomedical Imaging and Life Sciences (CNC.IBILI) Consortium, Univ...

C-Y. Lo, Y-P. Chao, K-H. Chen, K-H. Chou, and C-P. Lin Institute of Biomedical Imaging and Radiological Sciences, National Yang Ming University, Taipei, NA, Taiwan, Institute of Electrical Engineering, National Taiwan Univeristy, Taipei, Taiwan, Institute of Neuroscience, National Yang Ming Univeristy, Taipei, Taiwan, Institute of Biomedical Engineering, National Yang Ming Univeristy, Taipei, T...

Volumetric Space–Time Structure of Physiological Noise in BOLD fMRI Arno Solin, Simo Särkkä, Aapo Nummenmaa, Aki Vehtari, Toni Auranen, Simo Vanni, and Fa-Hsuan Lin Department of Biomedical Engineering and Computational Science, Aalto University, Espoo, Finland, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States, Advanced Magnetic Imag...

A. Klink, J. Heynens, B. Herranz, H. M. Sanders, G. J. Strijkers, K. Nicolay, M. Merkx, Z. Mallat, W. J. Mulder, and Z. A. Fayad Translational and Molecular Imaging Institute, Mount Sinai School of Medicine, New York, NY, United States, Biomedical NMR, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands, Vascular Imaging and Atherothrombosis, CNIC, M...

J OINTLY assessing data from multiple modalities is inherent to many problems in science and engineering, but it is especially relevant to the analysis of biomedical imaging data. Technologies for imaging are individually expensive; thus, ways to synergistically derive information from complementary modalities are clearly attractive. Moreover, for instance, since each brain imaging modality is ...

This paper shows promising results in the application of Convolutional Neural Networks (CNN) to biomedical imaging. Texture is often dominant in biomedical imaging and its analysis is essential to automatically obtain meaningful information. Therefore, we introduce a method using a Texture CNN for the classification of biomedical images. We test our approach on three datasets of liver tissues i...