Biomedical Texture Analysis: Fundamentals, Tools and Challenges

ISBN-13: 9780128121337 / Angielski / Miękka / 2017 / 430 str.

Adrien Depeursinge; Omar S. Al-Kadi; J. Ross Mitchell

Opis

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

Spis treści

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