Handbook of Texture Analysis: AI-Based Medical Imaging Applications [Paperback]

This volume presents important branches of texture analysis methods which find a proper application in AI-based medical image analysis.

1 An Exploratory Review on Local Binary Descriptors for Texture Classification 2 Precision Grading of Glioma: A System for Accurate Diagnosis and Treatment Planning 3 Enhancing Accuracy in Liver Tumor Detection and Grading: A Computer-Aided Diagnostic System 4 Texture Analysis in Radiology 5 Texture Analysis Using a Self-Organizing Feature Map 6 Sensor-Based Human Activity Recognition Analysis Using Machine Learning and Topological Data Analysis (TDA) 7 Application of Texture Analysis in Retinal OCT Imaging 8 Automation in Pneumonia Detection
9 Texture for Neuroimaging 10 A Multimodal MR-Based CAD System for Precise Assessment of Prostatic Adenocarcinoma 11 Texture Analysis in Cancer Prognosis

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 volume presents important branches of texture analysis methods which find a proper application in AI-based medical image analysis. This book:

• Discusses first-order, second-order statistical methods, local binary pattern (LBP) methods, and filter bank-based methods
• Covers spatial frequency-based methods, Fourier analysis, Markov random fields, Gabor filters, and Hough transformation
• Describes advanced textural methods based on DL as well as BD and advanced applications of texture to medial image segmentation