Deformable Registration Techniques for Thoracic CT Images : An Insight into Medical Image Registration / by Ali Imam Abidi, S.K. Singh

Von:

Abidi, Ali Imam [VerfasserIn]

Mitwirkende(r):

Singh, S.K [VerfasserIn]

Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Reihen: Springer eBook CollectionVerlag: Singapore : Springer Singapore, 2020Verlag: Singapore : Imprint: Springer, 2020Auflage: 1st ed. 2020Beschreibung: 1 Online-Ressource(VII, 135 p. 83 illus., 18 illus. in color.)ISBN:

9789811058370

Schlagwörter:

Andere physische Formen: 9789811058363 | 9789811058387 | Erscheint auch als: 9789811058363 Druck-Ausgabe | Erscheint auch als: 9789811058387 Druck-AusgabeDOI: DOI: 10.1007/978-981-10-5837-0Online-Ressourcen:

Zusammenfassung: 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.Zusammenfassung: This book focuses on novel approaches for thoracic computed tomography (CT) image registration and determination of respiratory 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. .PPN: PPN: 1699183767Package identifier: Produktsigel: ZDB-2-SCS | ZDB-2-SEB | ZDB-2-SXCS

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