Advanced Machine Vision Paradigms for Medical Image Analysis
Computer vision and machine intelligence paradigms are prominent in the domain of medical image applications, including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics. Medical image analysis and understanding are daunting tasks owing to the massive influx of multi-modal medical image data generated during routine clinal practice. Advanced computer vision and machine intelligence approaches have been employed in recent years in the field of image processing and computer vision. However, due to the unstructured nature of medical imaging data and the volume of data produced during routine clinical processes, the applicability of these meta-heuristic algorithms remains to be investigated.

Advanced Machine Vision Paradigms for Medical Image Analysis presents an overview of how medical imaging data can be analyzed to provide better diagnosis and treatment of disease. Computer vision techniques can explore texture, shape, contour and prior knowledge along with contextual information, from image sequence and 3D/4D information which helps with better human understanding. Many powerful tools have been developed through image segmentation, machine learning, pattern classification, tracking, and reconstruction to surface much needed quantitative information not easily available through the analysis of trained human specialists. The aim of the book is for medical imaging professionals to acquire and interpret the data, and for computer vision professionals to learn how to provide enhanced medical information by using computer vision techniques. The ultimate objective is to benefit patients without adding to already high healthcare costs.

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Advanced Machine Vision Paradigms for Medical Image Analysis
Computer vision and machine intelligence paradigms are prominent in the domain of medical image applications, including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics. Medical image analysis and understanding are daunting tasks owing to the massive influx of multi-modal medical image data generated during routine clinal practice. Advanced computer vision and machine intelligence approaches have been employed in recent years in the field of image processing and computer vision. However, due to the unstructured nature of medical imaging data and the volume of data produced during routine clinical processes, the applicability of these meta-heuristic algorithms remains to be investigated.

Advanced Machine Vision Paradigms for Medical Image Analysis presents an overview of how medical imaging data can be analyzed to provide better diagnosis and treatment of disease. Computer vision techniques can explore texture, shape, contour and prior knowledge along with contextual information, from image sequence and 3D/4D information which helps with better human understanding. Many powerful tools have been developed through image segmentation, machine learning, pattern classification, tracking, and reconstruction to surface much needed quantitative information not easily available through the analysis of trained human specialists. The aim of the book is for medical imaging professionals to acquire and interpret the data, and for computer vision professionals to learn how to provide enhanced medical information by using computer vision techniques. The ultimate objective is to benefit patients without adding to already high healthcare costs.

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Advanced Machine Vision Paradigms for Medical Image Analysis

Advanced Machine Vision Paradigms for Medical Image Analysis

Advanced Machine Vision Paradigms for Medical Image Analysis

Advanced Machine Vision Paradigms for Medical Image Analysis

Overview

Computer vision and machine intelligence paradigms are prominent in the domain of medical image applications, including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics. Medical image analysis and understanding are daunting tasks owing to the massive influx of multi-modal medical image data generated during routine clinal practice. Advanced computer vision and machine intelligence approaches have been employed in recent years in the field of image processing and computer vision. However, due to the unstructured nature of medical imaging data and the volume of data produced during routine clinical processes, the applicability of these meta-heuristic algorithms remains to be investigated.

Advanced Machine Vision Paradigms for Medical Image Analysis presents an overview of how medical imaging data can be analyzed to provide better diagnosis and treatment of disease. Computer vision techniques can explore texture, shape, contour and prior knowledge along with contextual information, from image sequence and 3D/4D information which helps with better human understanding. Many powerful tools have been developed through image segmentation, machine learning, pattern classification, tracking, and reconstruction to surface much needed quantitative information not easily available through the analysis of trained human specialists. The aim of the book is for medical imaging professionals to acquire and interpret the data, and for computer vision professionals to learn how to provide enhanced medical information by using computer vision techniques. The ultimate objective is to benefit patients without adding to already high healthcare costs.

Product Details

ISBN-13: 9780128192955
Publisher: Elsevier Science
Publication date: 08/13/2020
Series: Hybrid Computational Intelligence for Pattern Analysis and Understanding
Pages: 308
Product dimensions: 6.00(w) x 9.00(h) x (d)

About the Author

Dr Tapan K Gandhi is currently working as Associate Professor in the Dept. of Electrical Engineering, IIT Delhi and adjunct faculty in the school of AI and data science, IIT Jodhpur. He is also a research affiliate at MIT, USA. He received his Ph.D. fellowship from (MIT, USA) and obtained his Ph.D. in Biomedical Engineering from IIT Delhi. He has published papers in top ranking journals and has more than 150 publications in international journals and conference proceedings. He is PI & Co-PI of many funded projects from Industry, International Organizations as well as Govt. of India organizations.

Siddhartha Bhattacharyya is a Senior Researcher in the Faculty of Electrical Engineering and Computer Science of VSB Technical University of Ostrava, Czech Republic. He is also serving as the Scientific Advisor of Algebra University College, Zagreb, Croatia. Prior to this, he served as the Principal of Rajnagar Mahavidyalaya, Rajnagar, Birbhum. He was a professor at CHRIST (Deemed to be University), Bangalore, India, and also served as the Principal of RCC Institute of Information Technology, Kolkata, India. He is the recipient of several coveted national and international awards. He received the Honorary Doctorate Award (D. Litt.) from the University of South America and the SEARCC International Digital Award ICT Educator of the Year in 2017. He was appointed as the ACM Distinguished Speaker for the tenure 2018-2020. He has been appointed as the IEEE Computer Society Distinguished Visitor for the tenure 2021-2023. He has co-authored six books, co-edited 75 books, and has more than 300 research publications in international journals and conference proceedings to his credit.

Dr. Sourav De completed his PhD in Computer Science and Technology at the Indian Institute of Engineering & Technology, Shibpur, Howrah, India in 2015. He is currently an Associate Professor of Computer Science & Engineering at Cooch Behar Government Engineering College, West Bengal. He is a co-author of one book, the co-editor of twelve books, and has more than 54 research publications in internationally reputed journals, international edited books, international IEEE conference proceedings, and one patent to his credit. His research interests include soft computing, pattern recognition, image processing, and data mining. Dr. De is a senior member of IEEE and a member of ACM, Institute of Engineers (IEI), Computer Science Teachers Association (CSTA), Institute of Engineers and IAENG, Hong Kong. He is a life member of ISTE, India.

Debanjan Konar is an Assistant Professor at the Department of Computer Science and Engineering at Sikkim Manipal Institute of Technology, India. He is pursuing PhD from Indian Institute of Technology, Delhi and about to submit the PhD thesis on Quantum Self-supervised Neural Network for Medical Image Segmentation. His research interests include quantum computing, deep learning, machine learning, and medical image segmentation. He has published several papers in these areas in leading journals and IEEE international conferences. He is also a reviewer for various international journals and conferences.

Dr. Sandip Dey completed his PhD in Computer Science and Engineering at Jadavpur University, India in 2016. He is currently an Assistant Professor in the Department of Computer Science at Sukanta Mahavidyalaya, Jalpaiguri. He has more than 40 research publications in international journals, book chapters and conference proceedings to his credit. He has authored or edited four books, published by John Wiley & Sons and Elsevier. His research interests include soft computing, quantum computing and image analysis.

Table of Contents

1. Computer Aided Decision Support System for symmetry based prenatal congenital heart defects
2. Morphological Extreme Learning Machines applied to the detection and classification of mammary lesions
3. 4D Medical Image Analysis: A Systematic Study on Applications, Challenges and Future Research Directions
4. Comparative Analysis of Hybrid Fusion Algorithms using Neurocysticercosis, Neoplastic, Alzheimer's and Astrocytoma Disease affected Multimodality Medical Images
5. Binary Descriptors Design for the Automatic Detection of Coronary Arteries using Metaheuristics
6. A Cognitive Perception on Content Based Image Retrieval using Advanced Soft Computing Paradigm
7. Early detection of Parkinson’s Disease Using Data Mining Techniques from Multi-Modal Clinical Data
8. Contrast Improvement of Medical Images Using Advanced Fuzzy Logic Based Technique
9. Intelligent Heart Disease Prediction On Physical and Mental Parameters: A ML Based IoT and Big Data Application & Analysis

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