Hybrid Image Processing Methods For Medical Image Examination
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Hybrid Image Processing Methods for Medical Image Examination
In view of better results expected from examination of medical datasets (images) with hybrid (integration of thresholding and segmentation) image processing methods, this work focuses on implementation of possible hybrid image examination techniques for medical images. It describes various image thresholding and segmentation methods which are essential for the development of such a hybrid processing tool. Further, this book presents the essential details, such as test image preparation, implementation of a chosen thresholding operation, evaluation of threshold image, and implementation of segmentation procedure and its evaluation, supported by pertinent case studies. Aimed at researchers/graduate students in the medical image processing domain, image processing, and computer engineering, this book: Provides broad background on various image thresholding and segmentation techniques Discusses information on various assessment metrics and the confusion matrix Proposes integration of the thresholding technique with the bio-inspired algorithms Explores case studies including MRI, CT, dermoscopy, and ultrasound images Includes separate chapters on machine learning and deep learning for medical image processing
Magnetic Resonance Imaging
Magnetic Resonance Imaging: Recording, Reconstruction and Assessment gives a detailed overview of magnetic resonance imaging (MRI), along with its applications and challenges. The book explores the abnormalities in internal human organs using MRI techniques while also featuring case studies that illustrate measures used. In addition, it explores precautionary measures used during MRI based imaging, the selection of appropriate contrast agents, and the selection of the appropriate modality during the image registration. Sections introduce medical imaging, the use of MRI in brain, cardiac, lung and kidney detection, and also discuss both 2D and 3D imaging techniques and various MRI modalities. This volume will be of interest to researchers, engineers and medical professionals involved in the development and use of MRI systems. - Discusses challenges and issues faced, as well as safety precautions to be followed - Features case studies with benchmark MRIs existing in the literature - Introduces computer-based assessment (Machine Learning and Deep Learning) of the MRI based on its 2D slices
Digital Future of Healthcare
This book focuses on the applications of different digital platforms in the field of healthcare. It describes different devices used in digital healthcare, their benefits, diagnosis, use in treatment, and use cases related to mobile healthcare. Further, it covers machine and deep learning, blockchain technology, big data analytics as relevant to digital healthcare, telehealth technology, and digital applications in the field of push-and-pull pharma marketing. Overall, it enables readers to understand the basics of decision-making processes using digital techniques for the healthcare field. Features: Discusses various aspects of digitization of healthcare systems Examines deployment of machine learning including IoT and medical analytics Provides studies on the design, implementation, development, and management of intelligent healthcare systems Includes sensor-based digitization of healthcare data Reviews real-time advancement and challenges of digital communication in the field of healthcare This book is aimed at researchers and graduate students in healthcare, internet of things, machine learning, computer science, robotics, wearables, electrical engineering, and biomedical engineering.