Image Processing: Tensor Transform and Discrete Tomography with MATLAB (R)

Image Processing: Tensor Transform and Discrete Tomography with MATLAB® discusses novel approaches to image reconstruction in computer tomography, focusing on mathematical methods and avoiding overradiation during CT scans. It presents new concepts, methods, and examples, with MATLAB®-based programs and experimental results. The main approach differs from existing methods and results in more accurate reconstructions.

\n Format: Paperback / softback
\n Length: 466 pages
\n Publication date: 30 April 2018
\n Publisher: Taylor & Francis Ltd
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Image Processing: Tensor Transform and Discrete Tomography with MATLAB® is a comprehensive guide that delves into the realm of mathematical techniques in computer tomography (CT). By focusing on innovative approaches to image reconstruction on the Cartesian lattice, the book offers valuable insights to address the challenges associated with reconstructing images from a limited number of projections.

One of the key aspects of this text is its discussion of image processing techniques along parallel rays. These methods aim to expedite and enhance the accuracy of image reconstruction, thereby minimizing the over-exposure of the body during CT scans. The authors introduce several novel concepts, including methods for transferring the geometry of rays from the plane to the Cartesian lattice, the point map of projections, the particle and its field function, and the statistical model of averaging. These concepts provide a fresh perspective on image reconstruction and offer potential solutions to complex imaging problems.

The book is packed with numerous examples, MATLAB®-based programs, end-of-chapter problems, and experimental results of implementation. These resources not only reinforce the theoretical concepts but also enable readers to apply the knowledge gained in a practical setting. The authors' main approach for image reconstruction departs from conventional methods such as back-projection, iterative reconstruction, and Fourier and Radon filtering. Instead, they propose a system of linear equations or linear convolutions to process each projection, calculating the corresponding part of the 2-D tensor or paired transform of the discrete image. Subsequently, they describe how to calculate the inverse transform to obtain the reconstruction, resulting in more accurate reconstructions.

The proposed models for image reconstruction from projections are straightforward yet effective, leading to improved accuracy in image reconstructions. This book serves as a valuable resource for those interested in further research and in applying these methods to CT. It encourages readers to develop innovative applications of these techniques, contributing to the advancement of medical imaging and diagnostics.

In conclusion, Image Processing: Tensor Transform and Discrete Tomography with MATLAB® is a comprehensive and insightful guide that provides a solid foundation for those seeking to explore the realm of mathematical methods in CT. By offering novel approaches to image reconstruction, the book encourages readers to develop effective applications of these techniques in CT, ultimately advancing medical imaging and diagnostics.

\n Weight: 664g\n
Dimension: 154 x 229 x 33 (mm)\n
ISBN-13: 9781138076174\n \n