Breast image reconstruction and cancer detection using microwave imaging /

Search by :

ALL Author Subject ISBN/ISSN Advanced Search

Last search:

Breast image reconstruction and cancer detection using microwave imaging /

Patel, Hardik N., - Personal Name; Institute of Physics (Great Britain), - Personal Name; Suri, Jasjit S., - Personal Name; Ghodgaonkar, Deepak K., - Personal Name;

"Version: 20221201"--Title page verso.Includes bibliographical references.1. Introduction to breast cancer -- 1.1. Introduction to cancer -- 1.2. Worldwide cancer statistics -- 1.3. Breast cancer statistics -- 1.4. Breast anatomy and breast cancer -- 1.5. Summary2. Introduction to breast cancer detection techniques -- 2.1. Imaging modalities for breast cancer screening -- 2.2. Mammography -- 2.3. Ultrasound imaging -- 2.4. Magnetic resonance imaging -- 2.5. Positron emission tomography -- 2.6. Diffuse optical tomography -- 2.7. Electrical impedance tomography -- 2.8. Computed tomography (CT) -- 2.9. Microwave imaging -- 2.10. Comparison of mammography, MRI and ultrasound -- 2.11. Overview of image reconstruction methods -- 2.12. Summary3. Introduction to microwave imaging -- 3.1. Introduction -- 3.2. Introduction to passive microwave imaging -- 3.3. Microwave radiometry for cancer detection -- 3.4. Active microwave imaging -- 3.5. Summary4. Finite difference time domain method for microwave breast imaging -- 4.1. Overview of computational electromagnetic methods -- 4.2. Motivation -- 4.3. Overview of FDTD -- 4.4. Derivation of basic FDTD update equations -- 4.5. Polarization current density equation derivation for numerical breast phantom region -- 4.6. Electric field update equation derivation for numerical breast phantom region -- 4.7. Derivation of electric field update equations for PML region -- 4.8. Magnetic field update equations -- 4.9. Steps for FDTD implementation -- 4.10. Simulation parameters -- 4.11. Results -- 4.12. Summary5. 3D level set based optimization -- 5.1. Multiple frequency inverse scattering problem formulation -- 5.2. Introduction -- 5.3. Problem formulation -- 5.4. Review of previous work -- 5.5. Theoretical foundations -- 5.6. Single 3D level set function based optimization -- 5.7. Two 3D level set function based optimization -- 5.8. Simulation parameters -- 5.9. Results -- 5.10. Summary6. Method of moments -- 6.1. Theoretical background -- 6.2. Problem formulation -- 6.3. Computation reduction using group theory -- 6.4. Inverse scattering problem formulation -- 6.5. Simulation parameters and noise consideration -- 6.6. Results -- 6.7. Summary7. Finite difference time domain for microwave imaging -- 7.1. Introduction to finite difference time domain -- 7.2. Microwave image formation using confocal technique -- 7.3. Space-time beamforming -- 7.4. Removal of skin-breast artifact -- 7.5. FDTD based time reversal for microwave breast cancer detection -- 7.6. Summary8. Review of machine learning based image reconstruction for different imaging modalities -- 8.1. Introduction -- 8.2. Traditional image reconstruction techniques -- 8.3. Machine learning techniques for image reconstruction -- 8.4. Performance analysis of proposed approaches -- 8.5. Summary9. Review of machine learning based image reconstruction for microwave breast imaging -- 9.1. Motivation -- 9.2. Machine learning in microwave imaging -- 9.3. Flow of the machine learning based microwave breast imaging for cancer diagnosis -- 9.4. Variational Bayesian inversion for microwave breast imaging -- 9.5. Deep neural networks for microwave breast imaging -- 9.6. Summary10. Microwave image reconstruction methods -- 10.1. Levenberg-Marquardt method -- 10.2. Gauss-Newton method -- 10.3. Born iterative method -- 10.4. Stochastic optimization methods for microwave imaging -- 10.5. Summary11. The role of AI in diagnosis, treatment and monitoring of breast cancer during COVID-19 and ahead -- 11.1. Introduction -- 11.2. AI architectures -- 11.3. The role of artificial intelligence in diagnosis of breast cancer -- 11.4. The role of AI in treatment of breast cancer -- 11.5. The role of AI in monitoring of breast cancer -- 11.6. AI based integrated system for breast cancer management -- 11.7. Summary -- Appendix A. Numerical breast phantom, antenna placement and immersion (surrounding) medium -- Appendix B. Important derivations.This reference text explores cutting edge research into the detection of breast cancer using microwave imaging.Academic community working in biomedical imaging, electromagnetic and microwave imaging, breast cancer imaging, inverse scattering and optimization.Also available in print.Mode of access: World Wide Web.System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.Dr. Hardik N. Patel completed his PhD from Dhirubhai Ambani Institute of Information and communication technology in 2019. Master of Technology in Communication Systems from Sardar Vallabhbhai National Institute of Technology in 2010.Title from PDF title page (viewed on January 9, 2023).

Availability

No copy data

Detail Information

Series Title: -
Call Number: -
Publisher: : .,
Collation: 1 online resource (various pagings) :illustrations (some color).
Language: English
ISBN/ISSN: 9780750325929
Classification: 616.994490754
Content Type: -
Media Type: -
Carrier Type: -
Edition: -
Subject(s): Biomedical engineering.
TECHNOLOGY & ENGINEERING / Biomedical.
Breast
Breast Neoplasms
Microwave imaging in medicine.
Microwave Imaging.
Specific Detail Info: -
Statement of Responsibility: Hardik N. Patel, Deepak K. Ghodgaonkar, Jasjit S. Suri.

Other version/related

No other version available

File Attachment

No Data

Comments

You must be logged in to post a comment