Advanced Image Fusion Techniques for Medical Imaging: Trends, Applications, and Future Directions - Hardcover

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Dr. Palani Thanaraj Krishnan is an Associate Professor in the School of Computer Science & Engineering at Vellore Institute of Technology, Chennai. He holds a Ph.D. in Biomedical Engineering from Anna University and has over 14 years of teaching and research experience. His research interests include biomedical signal processing, image processing, data science, machine learning, and deep learning. He has published extensively, with over 40 publications in reputed journals and conferences. He is actively involved in funded research projects and serves as a peer reviewer for several international journals. As an IEEE Senior Member, he contributes to various professional societies and has received recognition for his work, including the VIHA Young Researcher Award in Biomedical Sciences and Engineering.

 Dr. Vijayarajan Rajangam is an Associate Professor at the Centre for Healthcare Advancement, Innovation, and Research, School of Electronics Engineering, Vellore Institute of Technology, Chennai. He did his doctoral research in medical image fusion for brain images. He has been in academics for over 25 years and has published forty-five research contributions in various SCI/Scopus journals and conferences. His research interests include computer vision, machine learning algorithms for signal and image processing, image fusion, bio-cryptography, emotion classification, and deep learning for image analysis.

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"Advanced Image Fusion Techniques for Medical Imaging: Trends, Applications, and Future Directions" is a comprehensive exploration of cutting-edge image fusion methodologies in the realm of medical imaging. This timely work addresses the growing need for advanced techniques to integrate and analyze multi-modal medical images, offering healthcare professionals and researchers a valuable resource for improving diagnostic accuracy and patient outcomes.

The book provides a thorough examination of both conventional and deep learning-based fusion strategies, covering a wide spectrum of applications in medical image processing. From noise reduction and image enhancement to segmentation and classification, the authors present a detailed analysis of how image fusion can be leveraged to overcome the limitations of individual imaging modalities. Real-world case studies and practical examples throughout the text demonstrate the tangible benefits of these techniques in clinical settings. Looking beyond current applications, this forward-thinking volume also explores emerging trends and future directions in medical image fusion. By addressing challenges and potential impacts on healthcare practices, the authors offer readers a glimpse into the future of medical imaging.

This book serves as an essential guide for researchers in biomedical engineering, medical professionals specializing in radiology and medical imaging, and graduate students in computer science, electrical engineering, biomedical engineering. This diverse audience reflects the book's comprehensive approach to advanced image fusion techniques in medical imaging, covering both theoretical foundations and practical applications.