Description
Advanced Machine Vision Paradigms for Medical Image Analysis
Hybrid Computational Intelligence for Pattern Analysis and Understanding Series
Coordinators: Gandhi Tapan K., Bhattacharyya Siddhartha, De Sourav, Konar Debanjan, Dey Sandip
Language: English
Subjects for Advanced Machine Vision Paradigms for Medical Image Analysis:
Keywords
Right ventricle–to–left ventricle ratio; 4D CT; 4D flow MRI; 4D medical imaging; 4D ultrasound scan; Alzheimer"s; ANFIS classifier; Applications; Astrocytoma; Automatic segmentation; Binary descriptors; Breast cancer image diagnosis; Cardiothoracic ratio; CBIR; CLAHE; Congenital heart defect; CT; Data mining; Decision support systems; DFRWT; Diagnosis; DTCWT; Early diagnosis; Fuzzy image processing; Fuzzy inference adaptive maximum likelihood estimation; HE stain; Health; Image registration; Image understanding; Kernel; Limitations and challenges; Machine learning techniques; Mathematical morphology; Median filter; Medical image analysis; Medical image processing; Medical image segmentation; Medical imaging; Metaheuristics; Modalities; Morphological kernels; MRI; Multimodal medical image fusion; Neoplastic; Neurocysticercosis; NSCT; NSST; Parkinson"s disease; PCNN; PET; Random-weighted neural networks; Semantic image retrieval; SPECT; Type-2 fuzzy set; Vessel enhancement
308 p. · 15x22.8 cm · Paperback
Description
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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.
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
Researchers, professionals, and graduate students in computer science & engineering, bioinformatics, and electrical engineering
Siddhartha Bhattacharyya is currently the principal of Rajnagar Mahavidyalaya, Birbhum, India. 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 has served VSB Technical University of Ostrava, Czech Republic as a Senior Research Scientist. 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 book
- Explores major emerging trends in technology which are supporting the current advancement of medical image analysis with the help of computational intelligence
- Highlights the advancement of conventional approaches in the field of medical image processing
- Investigates novel techniques and reviews the state-of-the-art in the areas of machine learning, computer vision, soft computing techniques, as well as their applications in medical image analysis
