Biomedical Texture Analysis

Adrien Depeursinge received a B.Sc. and M.Sc. degrees in electrical engineering from the Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland, in 2003 and 2005, respectively, with a specialization in signal and image processing. From 2006 to 2010, he performed his Ph.D. thesis on medical image analysis at the University Hospitals of Geneva (HUG). He then did a Postdoctoral Fellowship in the Department of Radiology at Stanford University. Starting from 2014, he holds a joint position as professor at the University of Applied Sciences Western Switzerland, Sierre (HES-SO) and as research associate in the Biomedical Imaging Group at EPFL. Dr. Depeursinge was the recipient of the 2011 German Association for Medical Informatics, Biometry and Epidemiology Award in medical informatics for his Ph.D. thesis. His research interests include N-dimensional texture analysis with control of image scales and directions as well as clinical workflows of image-based computer-aided diagnosis systems. Omar S Al-Kadi received the PhD in Biomedical Engineering from the University of Sussex (Brighton, UK) in 2010, and the MSc. and BSc. in Information Technology and Biomedical Engineering from the University of Canberra (Canberra, Australia) and Cairo University (Cairo, Egypt) in 2003 and 2001, respectively. In 2010 he joined King Abdullah II School for Information Technology at the University of Jordan (Amman, Jordan) as an Assistant Professor, and in 2011 he was a Visiting Researcher in the Center for Vision, Speech and Signal Processing at the University of Surrey (Guildford, UK). During the period from 2013 to 2015 he was a Research Fellow at the Institute of Biomedical Engineering at the University of Oxford (Oxford, UK), working on improving 3D Ultrasound-based drug delivery strategies for liver tumor analysis and segmentation. He was also a Visiting Professor at the Medical Image Processing lab within the Institute of BioEngineering at the Swiss Federal Institute of Technology - Lausanne (EPFL) from 2015-2016. Since 2015, he holds the position of Associate Professor at King Abdullah II School for Information Technology at the University of Jordan. His research interest include developing novel approaches in image processing and analysis, such as texture analysis, image classification and segmentation, with a particular interest in medical imaging. J Ross Mitchell, PhD, is a Professor of Radiology in the Mayo Clinic College of Medicine, a Senior Associate Consultant in the Department of Research at Mayo Clinic in Arizona, and an Adjunct Professor in the Department of Biomedical Informatics at Arizona State University. His research is focused on Medical Imaging Informatics - new algorithms to extract information from medical images to improve the diagnosis, treatment, visualization and monitoring of disease. He has extensive expertise translating academic research into clinically impactful products. He is a Co-Founder, and the Founding Scientist, of Calgary Scientific Inc. (CSI), a company with over 100 employees and a suite of FDA Class II cleared products for diagnostic state-of-the-art GPU-driven mobile tele-radiology. These products include intellectual property developed in his academic lab. They have been licensed by a variety of companies, including General Electric, Siemens, Fuji, Medtronic, Hitachi, IBM, Dell, Cisco, Sprint, Verizon, and others. CSI products are available in 12 languages and 33 countries worldwide

• 1. Fundamentals of Texture Processing for Biomedical Image Analysis2. Multi-Scale and Multi-Directional Biomedical Texture Analysis3. Biomedical Texture Operators and Aggregation Functions4. Deep Learning in Texture Analysis and its Application to Tissue Image Classification5. Fractals for Biomedical Texture Analysis6. Handling of Feature Space Complexity for Texture Analysis in Medical Images7. Rigid Motion Invariant Classification of 3D Textures8. An Introduction to Radiomics: An Evolving Cornerstone of Precision Medicine9. Deep Learning Techniques on Texture Analysis of Chest and Breast Images10. Analysis of Histopathology Images11. MaZda - a Framework for Biomedical Image Texture Analysis and Data Exploration12. QuantImage - An Online Tool for High-Throughput 3D Radiomics Feature Extraction in PET-CT13. Web-Based Tools for Exploring the Potential of Quantitative Imaging Biomarkers in Radiology