Full Description
This two-volume handbook provides a comprehensive view of texture analysis in both AI-based industrial and medical imaging applications. The first volume covers texture analysis for neuroradiology; information-theoretic entropy; chronic liver diseases; clinical management of focal liver lesions; abdominal imaging; optical coherence tomography images; thoracic imaging; prostate cancer; breast cancer; bladder cancer, quality evaluation of meat products; and detection of powdery mildew on strawberry leaves. The second volume covers Local Binary Descriptors for Texture Classification; Precision Grading of Glioma; Liver Tumor Detection and Grading; Texture Analysis in Radiology; Texture Analysis Using a Self-Organizing Feature Map; Sensor-Based Human Activity Recognition Analysis Using Machine Learning and Topological Data Analysis; Texture Analysis in Retinal OCT Imaging; Pneumonia Detection; Prostatic Adenocarcinoma; and Texture Analysis in Cancer Prognosis. Aimed at researchers, academics, and advanced students in biomedical engineering, image analysis, cognitive science, and computer science and engineering, this handbook is an essential reference for those looking to advance their understanding in this applied and emergent field.
Contents
Handbook of Texture Analysis: Generalized Texture for AI-Based Industrial Applications (Volume 1)
Part A: Texture Segmentation. 1. Texture-Based Segmentation for Extracting Image Shape Features. 2. Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields. 3. A Spatially Constrained Color-Texture Model for Image Segmentation. 4. Texture Segmentation Using Gabor Filters. Part B: Texture Classification. 5. Textural Features for Image Classification. 6. A Statistical Approach to Texture Classification. 7. Texture Classification From Random Features. 8. Texture Classification and Its Applications. 9. Texture Classification of Magnetic Resonance Images of the Human Brain. Part C: Shape From Texture. 10. Shape From Texture Without Boundaries. 11. General Principles for Shape From Texture. 12. 3D Shape From Texture. 13. The Equations for Recovering Shape From Texture. Part D: Texture Modeling. 14. Complex Building Description and Extraction Based on Hough Transformation and Cycle Detection. 15. Image Quilting for Texture Synthesis and Transfer. 16. Texture Analysis Using Gray Level Run Lengths. 17. The Use of Markov Random Fields as Models of Textures.
Handbook of Texture Analysis: AI-Based Medical Imaging Applications (Volume 2)
Part A: Texture in Medical Imaging. 1. Texture Analysis: A Review of Neurologic MR Imaging Applications.2. A Survey of Preprocessing and Feature Extraction Techniques for Radiographic Images. 3. CNN for Texture and Dynamic Texture Analysis. 4. Deep Learning in Texture Analysis and Its Application. Part B: Texture Synthesis. 5. Image Quilting for Texture Synthesis and Transfer. 6. Texture Synthesis Using Gray Level Run Lengths. 7. Gabor Filters as Texture Discriminator. 8. Hough Transformation and Texture Synthesis. 9. The Use of Markov Random Fields as Models of Textures. Part C: Texture Analysis and Its Applications. 11. Complex Building Description and Extraction Based on Hough Transformation and Cycle Detection. 12. Journey Toward Computer-Aided Diagnosis: Role of Image Texture Analysis. 13. A Review on Texture Analysis Methods in Biomedical Image Processing. 14. Image Filtering Techniques for Medical Image Post-Processing: An Overview. 15. Statistical Models of Appearance for Medical Image Analysis and Computer Vision. 16. Appearance Analysis for Diagnosing Malignant Lung Nodules. 17. Big Data, Decision Tree Induction, and Texture.
