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1. |
Record Nr. |
UNISA996465341403316 |
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Autore |
Abidi Ali Imam |
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Titolo |
Deformable Registration Techniques for Thoracic CT Images [[electronic resource] ] : An Insight into Medical Image Registration / / by Ali Imam Abidi, S.K. Singh |
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Pubbl/distr/stampa |
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Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (138 pages) |
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Disciplina |
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Soggetti |
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Optical data processing |
Pattern recognition |
Radiology |
Bioinformatics |
Computational biology |
Health informatics |
Computer graphics |
Image Processing and Computer Vision |
Pattern Recognition |
Imaging / Radiology |
Computer Appl. in Life Sciences |
Health Informatics |
Computer Graphics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Chapter 1. Introduction -- Chapter 2. Theoretical Background -- Chapter 3. A Moving Least Square Based Framework for Thoracic CT Image Registration -- Chapter 4. A Path Tracing and Deformity Estimation Methodology for Registration of Thoracic CT Image Sequences -- Chapter 5. Deformable Thoracic CT Images Sequence Registration using Strain Energy Minimization -- Chapter 6. Conclusion & Future Work. |
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Sommario/riassunto |
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This book focuses on novel approaches for thoracic computed tomography (CT) image registration and determination of respiratory |
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motion models in a range of patient scenarios. It discusses the use of image registration processes to remove the inconsistencies between medical images acquired using different devices. In the context of comparative research and medical analysis, these methods are of immense value in image registration procedures, not just for thoracic CT images, but for all types of medical images in multiple modalities, and also in establishing a mean respiration motion model. Combined with advanced techniques, the methods proposed have the potential to advance the field of computer vision and help improve existing methods. The book is a valuable resource for those in the scientific community involved in modeling respiratory motion for a large number of people. . |
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