Intelligent Computing Techniques in Biomedical Imaging: Methods, Case Studies, and Applications

Intelligent Computing Techniques in Biomedical Imaging: Methods, Case Studies, and Applications 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.

• Introduces Fourier theory and signal analysis tailored to applications in optical communications devices and systems
• Provides strong theoretical background, making it a ready resource for researchers and advanced students in optical communication and optical signal processing
• Starts from basic theory and then develops descriptions of useful applications

Dr. Bikesh Kumar Singh is Assistant Professor at the Department of Biomedical Engineering at National Institute of Technology Raipur, India. He has published more than 70 research papers in various international and national journals and conferences in the field of Biomedical Signal Processing and Biomedical Image Processing. He has reviewed dozens of research articles for reputed international journals such as Elsevier Biocybernetics and Biomedical Engineering, Data in Brief, Innovation and Research in Biomedical Engineering, Computers in Biology and Medicine, Future Generation Computer Systems, Informatics in Medicine Unlocked and other reputed journals such as The British Journal of Radiology, Journal of Intelligent Systems, Imaging Science Journal, IEEE Access, Elsevier Computer Methods and Programs in Biomedicine, IEEE Transactions on Image Processing, Springer Journal of Neural Computing and Applications, and others. Dr. Singh has been Head of the Department of Biomedical Engineering for 5 years.

Dr. G R Sinha is a Professor at Myanmar Institute of Information Technology (MIIT) Mandalay, Myanmar.
To his credit are over 200 research papers, book chapters, and books, including Analysis of Medical Modalities for Improved Diagnosis in Modern Healthcare, Biomedical Signal Processing for Healthcare Applications, Brain and Behavior Computing, and Data Science and Its Applications, Advances in Biometrics, and Cognitive Informatics, Volumes 1 and 2, AI-Based Brain Computer Interfaces, and Data Deduplication Approaches. He was Dean of Faculty and an Executive Council Member of CSVTU and has served as Distinguished Speaker in the field of Digital Image Processing for the Computer Society of India. His research interests include Applications of Machine Learning and Artificial Intelligence in Medical Image Analysis, Biomedical Signal Analysis, Computer Aided Diagnosis, Computer Vision, and Cognitive Science.