Patel, Hardik N., <br/><br/>
Breast image reconstruction and cancer detection using microwave imaging / 
Hardik N. Patel, Deepak K. Ghodgaonkar, Jasjit S. Suri. 
 - 1 online resource (various pagings) :  illustrations (some color). 
 - [IOP release $release] IOP ebooks. [2022 collection] .
 - IOP (Series).  Release 22. IOP ebooks.  2022 collection. .
<br/><br/> "Version: 20221201"--Title page verso. 
<br/><br/>Includes bibliographical references. 
<br/><br/>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. Summary 2. 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. Summary 3. 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. Summary 4. 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. Summary 5. 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. Summary 6. 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. Summary 7. 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. Summary 8. 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. Summary 9. 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. Summary 10. 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. Summary 11. 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. 
<br/><br/> This reference text explores cutting edge research into the detection of breast cancer using microwave imaging. 
<br/><br/> Academic community working in biomedical imaging, electromagnetic and microwave imaging, breast cancer imaging, inverse scattering and optimization. 
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<br/><br/><br/>Mode of access: World Wide Web. <br/>System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader. 
<br/><br/><br/>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. 
<br/><br/><label>ISBN: </label>9780750325929 9780750325912 
<br/><br/><label>Standard No.: </label>10.1088/978-0-7503-2592-9  doi 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Breast--Cancer--Imaging.<br/>Microwave imaging in medicine.<br/>Breast Neoplasms--diagnostic imaging.<br/>Microwave Imaging.<br/>Biomedical engineering.<br/>TECHNOLOGY & ENGINEERING / Biomedical.
<br/><br/><label>LC Class. No.: </label>RC280.B8  / P383 2022eb 
<br/><br/><label>Dewey Class. No.: </label>616.994490754 
<br/><br/><label>National Library of Medicine Call No.: </label>WP 39  / P295b 2022eb