Handbook of Texture Analysis

The major goals of texture research in computer vision are to understand, model, and process texture, and ultimately, to simulate the human visual learning process using computer technologies. In the last decade, artificial intelligence has been revolutionized by machine learning and big data approaches, outperforming human prediction on a wide range of problems. In particular, deep learning convolutional neural networks (CNNs) are particularly well suited to texture analysis. This book examines four major application domains related to texture analysis and their relationship to AI-based industrial applications: texture classification, texture segmentation, shape from texture, and texture synthesis. This volume: - Discusses texture-based segmentation for extracting image shape features, modeling and segmentation of noisy and textured images, spatially constrained color-texture model for image segmentation, and texture segmentation using Gabor filters - Examines textural features for image classification, a statistical approach for classification, texture classification from random features, and applications of texture classifications - Describes shape from texture, including general principles, 3D shapes, and equations for recovering shape from texture - Surveys texture modeling, including extraction based on Hough transformation and cycle detection, image quilting, gray level run lengths, and use of Markov random fields Aimed at researchers, academics, and advanced students in biomedical engineering, image analysis, cognitive science, and computer science and engineering, this is an essential reference for those looking to advance their understanding in this applied and emergent field.

Ayman El-Baz is a Distinguished Professor at University of Louisville, Kentucky, United States and University of Louisville at Alamein International University (UofL-AIU), New Alamein City, Egypt. Dr. El-Baz earned his B.Sc. and M.Sc. degrees in electrical engineering in 1997 and 2001, respectively. He earned his Ph.D. in electrical engineering from the University of Louisville in 2006. Dr. El-Baz was named as a Fellow for IEEE, Coulter, AIMBE and NAI for his contributions to the field of biomedical translational research. Dr. El-Baz has almost two decades of hands-on experience in the fields of bio-imaging modeling and non-invasive computer-assisted diagnosis systems. He has authored or coauthored more than 700 technical articles. Mohammed Ghazal is a Professor and Chairman of the Department of Electrical, Computer, and Biomedical Engineering at the College of Engineering, Abu Dhabi University, UAE. His research areas are bioengineering, image and video processing, and smart systems. He received his Ph.D and M.A.Sc in Electrical and Computer Engineering (ECE) from Concordia University in Montreal Canada in 2010 and 2006, respectively, and his B.Sc. in Computer Engineering from the American University of Sharjah (AUS) in 2004. He has received multiple awards including the Distinguished Faculty Award of Abu Dhabi University in 2017 and 2014. Dr. Ghazal has authored or co-authored over 70 publications in recognized international journals and conferences including IEEE Transactions in Image Processing, IEEE Transactions in Circuits and Systems for Video Technology, IEEE Transactions in Consumer Electronics, Elsevier's Renewable Energy Reviews, and Springer's Multimedia Tools and Applications. Jasjit S. Suri is an innovator, scientist, visionary, industrialist and an internationally known world leader in biomedical engineering. Dr. Suri has spent over 25 years in the field of biomedical engineering/devices and its management. He received his Ph.D. from the University of Washington, Seattle and his Business Management Sciences degree from Weatherhead, Case Western Reserve University, Cleveland, Ohio. Dr. Suri was crowned with President's Gold medal in 1980 and made Fellow of the American Institute of Medical and Biological Engineering for his outstanding contributions. In 2018, he was awarded the Marquis Life Time Achievement Award for his outstanding contributions and dedication to medical imaging and its management.

1 Texture Analysis in Neuroradiology 2 Information Theoretic Entropy Approaches and Their Applications to Texture Analysis 3 Texture Analysis in Chronic Liver Diseases 4 Role of Texture Analysis in the Clinical Management of Focal Liver Lesions 5 Texture Analysis in Abdominal Imaging 6 Texture Modeling in Optical Coherence Tomography Images 7 Texture Analysis in Thoracic Imaging 8 Applications of Texture Analysis in Prostate Cancer 9 Texture Analysis for Breast Ultrasound Using Conventional Method and Deep Learning 10 Texture Analysis and Machine Learning on MRI for the Quality Evaluation of Meat Products 11 Comparison of Image Processing Techniques with Supervised Machine Learning vs. Deep Learning Based on Texture Analysis to Detect Powdery Mildew on Strawberry Leaves 12 A Radiomic Features-Based Pipeline for Accurate Bladder Cancer Staging