Optical Spectroscopy and Imaging for Cancer Diagnostics

Optical Spectroscopy and Imaging for Cancer Diagnostics

癌症诊断光谱学与成像：基础、进展和挑战
生物医学工程学

原价：

￥ 1715

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平台大促低至8折优惠

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作者

出版社

World Scientific Publishing

出版时间

2023年01月11日

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ＩＳＢＮ

9789811258954

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页码

560 pp

语种

英文

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图书简介

This is an interdisciplinary book that presents the applications of novel laser spectroscopy and imaging techniques for the detection of cancers recently developed by some of the world’s most renown researchers. The book consists of three parts and a total of 16 chapters. Each chapter is written by leading experts who are actively seeking to develop novel spectroscopic and analytical methods for cancer detection and diagnosis.In Part I, the authors present fundamentals on optics, atoms and molecules, biophysics, cancer and machine learning. These chapters are intended for those who are not experts in the field but wish to learn about fundamentals’ aspects of some of the key topics that are addressed in this book. Particular attention has been given to providing key references for those who wish to go further into the fundamental aspects of atoms and molecules, light-matter interaction, optical instrumentation, machine learning and cancer.In Part II, the authors present key applications of various laser spectroscopic methods in cancer diagnosis. They have provided recent progress in cancer diagnostics obtained by combining laser spectroscopy and machine learning for the analysis of the spectra acquired from biomedical tissues and biofluids.In Part III, the authors present chapters that discuss key developments in the applications of various laser imaging techniques for cancer detection.This is one of the few books that addresses cancer detection and diagnosis using laser spectroscopic tools with an eye on providing the reader the scientific tools to better understand the novelty that laser spectroscopy and imaging can provide.Key FeaturesThis is one of the few books that addresses cancer detection and diagnosis using laser spectroscopic toolsProvides specific tools to better understand the novelty that laser spectroscopy and imaging can provideProvides fundamentals first and then key developments made by some of the leading experts

Fundamentals: Catch Us if You Can — Cancer Biology and Optical Assessment of Human Tumors (M J Morin); Fundamentals of Light Interaction with Atoms and Molecules (B Bayram, Marjatta Lyyra and Noureddine Melikechi); Biomedical Optics in a Nutshell (C Boudoux); Fundamentals of Optical Instrumentation for Spectroscopic and Imaging Applications (E H Amara, S Messaoud Aberkane and Noureddine Melikechi); Fundamentals of Classification with Machine Learning (D D Pokrajac); Optical Spectroscopy: Exogenous Fluorescence Polarization Imaging for Cancer Detection (Androniki Mitrou and Anna N Yaroslavsky); Laser-Ablation Spectroscopy for Imaging of Tumor Markers and Nanoparticle Labels (Pavel Pořízka, Pavlína Modlitbová, Noureddine Melikechi and Jozef Kaiser); Application of ATR-FTIR Spectroscopy to the Study of Blood Sera of Patients with Breast Cancer (Vera Sitnikova, Tatiana Nosenko and Mayya Uspenskaya); Diagnosis and Staging of Cancers Using Laser-Induced Breakdown Spectroscopy and Machine Learning (Xue Chen, Xiaohui Li and N Melikechi); Raman Spectroscopy and Machine Learning for Diagnosis and Monitoring of Cancer (Michael Greenop, Carlos Meza Ramirez and Ihtesham Ur Rehman); Optical Imaging: Application of Fluorescence Lifetime Imaging in Cancer Diagnosis (Lixin Liu, Ping Xue, and Junle Qu); The Role of Machine learning and Time-Resolved Autofluorescence Imaging for Intraoperative Cancer Margin Delineation (Jakob Unger, Mark Marsden, Takanori Fukazawa and Laura Marcu); Laser Induced Breakdown Imaging of Biomedical Samples: A Short Review and Perspectives (Vincent Motto-Ros, Lucie Sancey, Vincent Bonneterre and Benoit Busser); Raman Imaging, Deep Learning, and Applications to Cancer Detection (Daniel Greenfield, Saara Luna, Fotis Iliopoulos, Maria Alice Maciel Tabosa and Conor L Evans); Skin Cancer Detection Using Optical Coherence Tomography (Brandon Lukas, Julia Roma May and Kamran Avanaki); White Light Interference Phase Microscopy with Machine Learning Model for the Study of Breast Cancer Cells and Their Classification (Dalip Singh Mehta)

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