Advances in Computational Techniques for Biomedical Image Analysis
1st Edition
Methods and Applications
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Description
Advances in Computational Techniques for Biomedical Image Analysis: Method and Applications focuses on post-acquisition challenges such as image enhancement, detection of edges and objects, analysis of shape, quantification of texture and sharpness, and pattern analysis. It discusses the archiving and transfer of images, presents a selection of techniques for the enhancement of contrast and edges, for noise reduction and for edge-preserving smoothing. It examines various feature detection and segmentation techniques, together with methods for computing a registration or normalization transformation.
Advances in Computational Techniques for Biomedical Image Analysis: Method and Applications is ideal for researchers and post graduate students developing systems and tools for health-care systems.
Key Features
- Covers various challenges and common research issues related to biomedical image analysis
- Describes advanced computational approaches for biomedical image analysis
- Shows how algorithms are applied to a broad range of application areas, including Chest X-ray, breast CAD, lung and chest, microscopy and pathology, etc.
- Explores a range of computational algorithms and techniques, such as neural networks, fuzzy sets, and evolutionary optimization
- Explores cloud based medical imaging together with medical imaging security and forensics
Readership
Medical imaging researchers, graduate students, clinical researchers
Table of Contents
1. Introduction to computational techniques in biomedical image analysis
2. Medical Imaging modalities
3. Multi-Modal Medical Image Fusion using Deep Learning
4. Medical Image Fusion Framework for Neuro Brain Analysis
5. Automated Detection of Intracranial Hemorrhage in Non-contrast Head CT
6. fMRI analysis to monitor the growth of brain tumor
7. Segmentation techniques for the diagnosis of intervertebral disc
8. Heart Sound Classification using Mel-frequency Cepstral Coefficients and Convolutional Neural Network
9. Comparative analysis of Classification Techniques for Brain MRI Images
10. Hybrid Feature Selection-based feature fusion for Liver disease Classification on Ultrasound Images
11. Comparison of class separability measures with different kernel functions of SVM for breast density classification
12. Radiomics: Approach to Precision Medicine
13. DCT based Compressed Sensing Recovery Strategies in Medical Image Compression: A Comparative Study
14. Segmentation Based Compression Techniques for Medical Images
15. Systematic Survey of Compression Algorithms in Medical Imaging
16. Multi-Level Encryption for securing medical image communication
17. A Modified Digital Signature Algorithm to Improve the Bio-Medical Image Integrity in Cloud Environment
18. Medical Imaging Security and Forensics
Details
- No. of pages:
- 326
- Language:
- English
- Copyright:
- © Academic Press 2020
- Published:
- 1st June 2020
- Imprint:
- Academic Press
- Paperback ISBN:
- 9780128200247
About the Author
Deepika Koundal
Dr. Deepika Koundal is currently associated with the University of Petroleum and Energy Studies, Dehradun. She has 12 years of teaching and research experience at many reputed Universities of India. She received her Bachelor Degree in Computer Science and Engineering from Kurukshetra University, Kurukshetra, India and subsequently her Masters and Doctorate Degrees in Computer Science & Engineering from Panjab University, Chandigarh, India. Her Ph.D. thesis is focused on Automated delineation of thyroid nodules in Ultrasound Images. She is actively pursuing research in Medical Image Processing. She is the awardee of Research excellence award given by Chitkara University in 2019. She also received the recognition and honorary membership from Neutrosophic Science Association from University of Mexico for her outstanding publication. She has published more than 30 research articles in reputed SCI and Scopus indexed journals and conferences.
Affiliations and Expertise
Assistant Professor, Department of Virtualization, School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India
Savita Gupta
Prof. Savita Gupta (SG) is a Professor in the Department of Computer Science and Engineering, University Institute of Engineering and Technology (UIET), Panjab University. She has received her M.E degree from Thapar University and Ph.D. degree in Computer Science and Engineering from the Punjab Technical University, Jalandhar. She has been working in Panjab University for more than 11 years and is currently designated as Director, UIET, Panjab University. She is passionately performing her research activities in the field of Signal and Image Processing, Medical Image Analysis, Wavelets based Signal and Image processing, Artificial Intelligence and Cognitive neuroscience. Her contributions in the field of speckle noise reduction in Ultrasound images is widely acknowledged in the field of biomedical engineering. She has contributed more than 40 research papers in reputed journals and more than 30 conference articles are there to her credit. So far, 5 students have completed their Ph.D. and 9 students are currently pursuing Ph.D under her supervision. She has an h-index of 66 and i10 index of 481.
Affiliations and Expertise
Director, University Institute of Engineering Technology, Panjab University, Chandigarh, India