The expanded and revised edition will split Chapter 4 to include more details and examples in FMRI, DTI, and DWI for MR image modalities. The book will also expand ultrasound imaging to 3-D dynamic contrast ultrasound imaging in a separate chapter.
A new chapter on Optical Imaging Modalities elaborating microscopy, confocal microscopy, endoscopy, optical coherent tomography, fluorescence and molecular imaging will be added. Another new chapter on Simultaneous Multi-Modality Medical Imaging including CT-SPECT and CT-PET will also be added. In the image analysis part, chapters on image reconstructions and visualizations will be significantly enhanced to include, respectively, 3-D fast statistical estimation based reconstruction methods, and 3-D image fusion and visualization overlaying multi-modality imaging and information. A new chapter on Computer-Aided Diagnosis and image guided surgery, and surgical and therapeutic intervention will also be added.
A companion site containing power point slides, author biography, corrections to the first edition and images from the text can be found here: wiley.com/public/sci_tech_med/medical_image/
Send an email to: Pressbooks@ieee.org to obtain a solutions manual. Please include your affiliation in your email.
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ATAM P. DHAWAN, PHD, is Distinguished Professor in the Electrical and Computer Engineering Department at New Jersey Institute of Technology. He teaches courses in biomedical engineering and has supervised approximately fifty graduate students, including twenty-one PhD students. Dr. Dhawan is a Fellow of the IEEE and the recipient of numerous national and international awards. He has published more than 200 research articles in refereed journals, conference proceedings, and edited books. Dr. Dhawan has chaired numerous study sections and review panels for the National Institutes of Health in biomedical computing and medical imaging and health informatics. His current research interests are medical imaging, multi-modality medical image analysis, multi-grid image reconstruction, wavelets, genetic algorithms, neural networks, adaptive learning, and pattern recognition.
Now updated?the most comprehensive reference of medical imaging modalities and image analysis techniques
The last two decades have witnessed revolutionary advances in medical imaging and computerized medical image processing. With the advent and enhancement of numerous sophisticated medical imaging modalities, intelligent processing of multi-dimensional images has become critical in radiological and diagnostic applications.
This benchmark text takes a unique, all-inclusive approach to the topic?one that weaves together medical physics, medical imaging instrumentation, and advanced image analysis methods. This Second Edition is completely revised and expanded to provide a broader foundation, helping engineers, medical professionals, and students alike understand medical imaging principles, perform intelligent image interpretation, and navigate the intricacies of instrumentation, data collection, image reconstruction, and computerized image analysis for radiological computer-aided evaluation and diagnosis. New chapters cover:
This updated edition presents individual chapters focused on x-ray, MRI, nuclear medicine, and ultrasound imaging modalities with additional details and recent advances. In addition, chapters on image reconstructions and visualizations have been significantly enhanced to include, respectively, 3-D statistical estimation?based reconstruction methods, feature classification and multi-modality image visualization. Examples with clinical images for medical image analysis and computer-aided diagnosis are provided throughout, as well as skill-building MATLAB® exercises.
An ideal learning tool, this state-of-the-art resource can be used for one- or two-semester based senior undergraduate and/or graduate-level courses. Students in medical imaging and image processing, electrical and computer engineering, computer science, and biomedical engineering as well as physicians, medical physicists, and researchers will gain the knowledge to master the complexities of today's radiological and diagnostic applications.
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