Computerized recognition and quantification of texture information has been an active research domain for the past 50 years, with some of the pioneering work still widely used today. Recently, the increasing ubiquity of imaging data has driven the need for powerful image analysis approaches to convert this data into knowledge. One of the most promising application domains is biomedical imaging, which is a key enabling technology for precision medicine (e.g., radiomics and digital histopathology) and biomedical discovery (e.g., microscopy). The colossal research efforts and progress made in the general domain of computer vision have led to extremely powerful data analysis systems. Biomedical imaging relies upon well-defined acquisition protocols to produce images. This is quite different from general photography. Consequently, the analysis of biomedical images requires a paradigm change to account for the quantitative nature of the imaging process. Texture analysis is a broadly applicable, powerful technology for quantitative analysis of biomedical images.
This book provides a thorough background on texture analysis for graduate students, and biomedical engineers from both industry and academia who have basic image processing knowledge. Medical doctors and biologists with no background in image processing will also find available methods and software tools for analyzing textures in medical images.
By bringing together experts in data science, medicine, and biology, we hope that this book will actively promote the translation of incredibly powerful data analysis methods into several breakthroughs in biomedical discovery and noninvasive precision medicine.
- Define biomedical texture precisely and describe how it is different from general texture information considered in computer vision
- Define the general problem to translate 2D and 3D texture patterns from biomedical images to visually and biologically relevant measurements
- Describe with intuitive concepts how the most popular biomedical texture analysis approaches (e.g., gray-level matrices, fractals, wavelets, deep convolutional neural networks) work, what they have in common, and how they are different
- Identify the strengths, weaknesses, and current challenges of existing methods including both handcrafted and learned representations, as well as deep learning. The goal is to establish foundations for building the next generation of biomedical texture operators
- Showcase applications where biomedical texture analysis has succeeded and failed
- Provide details on existing, freely available texture analysis software. This will help experts in medicine or biology develop and test precise research hypothesis
Biomedical engineers, medical imaging scientists, Radiologists and clinicians
1. Fundamentals of Texture Processing for Biomedical Image Analysis,
Adrien Depeursinge, Julien Fageot and Omar Al Kadi
2. Multi-Scale and Multi-Directional Biomedical Texture Analysis
3. Biomedical Texture Operators and Aggregation Functions
4. Deep Learning in Texture Analysis and its Application to Tissue Image Classification
Vincent Andrearczyk and Paul F. Whelan
5. Fractals for Biomedical Texture Analysis,
Omar Al Kadi
6. Handling of Feature Space Complexity for Texture Analysis in Medical Images
Yang Song and Weidong Cai
7. Rigid Motion Invariant Classification of 3D Textures
Sanat Upadhyay, Saurabh Jain and Manos Papadakis
8. An Introduction to Radiomics: An Evolving Cornerstone of Precision Medicine
Sara Ranjbar and Ross Mitchell
9. Deep Learning Techniques on Texture Analysis of Chest and Breast Images,
Jie-Zhi Cheng Chung-Ming Chen and Dinggang Shen
10. Analysis of Histopathology Images
Oscar Jimenez-del-Toro, Sebastian Otalora, Mats Andersson, Kristian Euren, Martin Hedlund, Mikael Rousson, Henning M ̈uller and Manfredo Atzori
11. MaZda - a Framework for Biomedical Image Texture Analysis and Data Exploration
Piotr M. Szczypínski and Artur Klepaczko
12. QuantImage - An Online Tool for High-Throughput 3D Radiomics Feature Extraction in PET-CT
Yashin Dicente Cid, Joel Castelli, Roger Schaer, Nathaniel Schery, Anastasia Pomoni John Prior and Adrien Depeursinge
13. Web-Based Tools for Exploring the Potential of Quantitative Imaging Biomarkers in Radiology
Roger Schaer, Yashin Dicente Cid, Emel Alkim, Sheryl John, Daniel L.Rubin and Adrien Depeursinge
- No. of pages:
- © Academic Press 2018
- 4th September 2017
- Academic Press
- Paperback ISBN:
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.
University of Applied Sciences, Western Switzerland (HES-SO).
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.
Associate Professor, King Abdullah II School for Information Technology, University of Jordan
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
Mayo Clinic College of Medicine, and Department of Biomedical Informatics at Arizona State University, USA