Intelligent Computing Techniques in Biomedical Imaging provides comprehensive and state-of-the-art applications of Computational Intelligence techniques used in biomedical image analysis for disease detection and diagnosis. The book offers readers a stepwise approach from fundamental to advanced techniques using real-life medical examples and tutorials. The editors have divided the book into five sections, from prerequisites to case studies.
Section I presents the prerequisites, where the reader will find fundamental concepts needed for advanced topics covered later in this book. This primarily includes a thorough introduction to Artificial Intelligence, probability theory and statistical learning. The second section covers Computational Intelligence methods for medical image acquisition and pre-processing for biomedical images. In this section, readers will find AI applied to conventional and advanced biomedical imaging modalities such as X-rays, CT scan, MRI, Mammography, Ultrasound, MR Spectroscopy, Positron Emission Tomography (PET), Ultrasound Elastography, Optical Coherence Tomography (OCT), Functional MRI, Hybrid Modalities, as well as pre-processing topics such as medical image enhancement, segmentation, and compression. Section III covers description and representation of medical images. Here the reader will find various categories of features and their relevance in different medical imaging tasks. This section also discusses feature selection techniques based on filter method, wrapper method, embedded method, and more.
The fourth section covers Computational Intelligence techniques used for medical image classification, including Artificial Neural Networks, Support Vector Machines, Decision Trees, Nearest Neighbor Classifiers, Random Forest, clustering, extreme learning, Convolution Neural Networks (CNN), and Recurrent Neural Networks. This section also includes a discussion of computer aided diagnosis and performance evaluation in radiology.
The final section of Intelligent Computing Techniques in Biomedical Imaging provides readers with a wealth of real-world Case Studies for Computational Intelligence techniques in applications such as neuro-developmental disorders, brain tumor detection, breast cancer detection, bone fracture detection, pulmonary imaging, thyroid disorders, imaging technologies in dentistry, diagnosis of ocular diseases, cardiovascular imaging, and multimodal imaging.
1. Chapter 1 Introduction to intelligent medical image analysis - p. 3 2. Chapter 2 Introduction to intelligent techniques and applications - p. 11 3. Chapter 3 Basics of probability and statistics using Python - p. 39 4. Chapter 4 Computer-aided diagnosis and performance evaluation - p. 71 5. Chapter 5 Feature extraction and feature selection in medical images - p. 83 6. Chapter 6 Deployment, feature extraction, and selection in computer vision and medical imaging - p. 99 7. Chapter 7 Image segmentation using template matching method for automated classification of knee osteoarthritis grades☆☆Research article. - p. 113 8. Chapter 8 Applications of intelligent techniques in pulmonary imaging - p. 131 9. Chapter 9 Digital breast tomosynthesis versus two-dimensional mammography: a comparison on breast imaging for cancer screening - p. 139 10. Chapter 10 Study on Alzheimer’s detection using machine learning and deep learning techniques - p. 149 11. Chapter 11 Early detection of pediatrics renal disease using ultrasound imaging - p. 163 12. Chapter 12 Imaging in neurodevelopmental disorders - p. 177 13. Chapter 13 A computational intelligence-based approach for detecting drowsiness in sleep apnea patients using multimodal bio-signals - p. 185 14. Chapter 14 Comparison of deep transfer learning models to detect obstructive sleep apnea with single-channel electrocardiogram - p. 201 15. Chapter 15 Anxiety brain topographic maps and its classification using quantitative electroencephalography - p. 217 16. Chapter 16 Smart computing in brain-computer interface and neuroscientific research: opportunities, methods, and challenges - p. 239 17. Chapter 17 Automated detection of diabetic retinopathy from gray-scale fundus images using GLCM and GLRLM-based textural features—a comparative study - p. 251 18. Chapter 18 Leg postural identification framework based on one-dimensional force data using machine learning models - p. 261 19. Chapter 19 Artificial intelligence for biomedical application associated with fluid-material dynamics - p. 273 20. Chapter 20 MRI and SPECT image fusion based on adaptive fusion rule in transform domain - p. 281 21. Index - p. 295
Intelligent Computing Techniques in Biomedical Imaging provides comprehensive and state-of-the-art applications of Computational Intelligence techniques used in biomedical image analysis for disease detection and diagnosis. The book offers readers a stepwise approach from fundamental to advanced techniques using real-life medical examples and tutorials. The editors have divided the book into five sections, from prerequisites to case studies.
Section I presents the prerequisites, where the reader will find fundamental concepts needed for advanced topics covered later in this book. This primarily includes a thorough introduction to Artificial Intelligence, probability theory and statistical learning. The second section covers Computational Intelligence methods for medical image acquisition and pre-processing for biomedical images. In this section, readers will find AI applied to conventional and advanced biomedical imaging modalities such as X-rays, CT scan, MRI, Mammography, Ultrasound, MR Spectroscopy, Positron Emission Tomography (PET), Ultrasound Elastography, Optical Coherence Tomography (OCT), Functional MRI, Hybrid Modalities, as well as pre-processing topics such as medical image enhancement, segmentation, and compression. Section III covers description and representation of medical images. Here the reader will find various categories of features and their relevance in different medical imaging tasks. This section also discusses feature selection techniques based on filter method, wrapper method, embedded method, and more.
The fourth section covers Computational Intelligence techniques used for medical image classification, including Artificial Neural Networks, Support Vector Machines, Decision Trees, Nearest Neighbor Classifiers, Random Forest, clustering, extreme learning, Convolution Neural Networks (CNN), and Recurrent Neural Networks. This section also includes a discussion of computer aided diagnosis and performance evaluation in radiology.
The final section of Intelligent Computing Techniques in Biomedical Imaging provides readers with a wealth of real-world Case Studies for Computational Intelligence techniques in applications such as neuro-developmental disorders, brain tumor detection, breast cancer detection, bone fracture detection, pulmonary imaging, thyroid disorders, imaging technologies in dentistry, diagnosis of ocular diseases, cardiovascular imaging, and multimodal imaging.
1. Chapter 1 Introduction to intelligent medical image analysis - p. 3 2. Chapter 2 Introduction to intelligent techniques and applications - p. 11 3. Chapter 3 Basics of probability and statistics using Python - p. 39 4. Chapter 4 Computer-aided diagnosis and performance evaluation - p. 71 5. Chapter 5 Feature extraction and feature selection in medical images - p. 83 6. Chapter 6 Deployment, feature extraction, and selection in computer vision and medical imaging - p. 99 7. Chapter 7 Image segmentation using template matching method for automated classification of knee osteoarthritis grades☆☆Research article. - p. 113 8. Chapter 8 Applications of intelligent techniques in pulmonary imaging - p. 131 9. Chapter 9 Digital breast tomosynthesis versus two-dimensional mammography: a comparison on breast imaging for cancer screening - p. 139 10. Chapter 10 Study on Alzheimer’s detection using machine learning and deep learning techniques - p. 149 11. Chapter 11 Early detection of pediatrics renal disease using ultrasound imaging - p. 163 12. Chapter 12 Imaging in neurodevelopmental disorders - p. 177 13. Chapter 13 A computational intelligence-based approach for detecting drowsiness in sleep apnea patients using multimodal bio-signals - p. 185 14. Chapter 14 Comparison of deep transfer learning models to detect obstructive sleep apnea with single-channel electrocardiogram - p. 201 15. Chapter 15 Anxiety brain topographic maps and its classification using quantitative electroencephalography - p. 217 16. Chapter 16 Smart computing in brain-computer interface and neuroscientific research: opportunities, methods, and challenges - p. 239 17. Chapter 17 Automated detection of diabetic retinopathy from gray-scale fundus images using GLCM and GLRLM-based textural features—a comparative study - p. 251 18. Chapter 18 Leg postural identification framework based on one-dimensional force data using machine learning models - p. 261 19. Chapter 19 Artificial intelligence for biomedical application associated with fluid-material dynamics - p. 273 20. Chapter 20 MRI and SPECT image fusion based on adaptive fusion rule in transform domain - p. 281 21. Index - p. 295
