Advances in Computerized Analysis in Clinical and Medical Imaging
暫譯: 臨床與醫學影像電腦化分析的進展
Peter, J. Dinesh, Fernandes, Steven Lawrence, Thomaz, Carlos Eduardo
- 出版商: CRC
- 出版日期: 2019-11-14
- 售價: $5,550
- 貴賓價: 9.5 折 $5,273
- 語言: 英文
- 頁數: 280
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 1138333298
- ISBN-13: 9781138333291
海外代購書籍(需單獨結帳)
商品描述
Advances in Computerized Analysis in Clinical and Medical Imaging book is devoted for spreading of knowledge through the publication of scholarly research, primarily in the fields of clinical & medical imaging. The types of chapters consented include those that cover the development and implementation of algorithms and strategies based on the use of geometrical, statistical, physical, functional to solve the following types of problems, using medical image datasets: visualization, feature extraction, segmentation, image-guided surgery, representation of pictorial data, statistical shape analysis, computational physiology and telemedicine with medical images.
This book highlights annotations for all the medical and clinical imaging researchers’ a fundamental advances of clinical and medical image analysis techniques. This book will be a good source for all the medical imaging and clinical research professionals, outstanding scientists, and educators from all around the world for network of knowledge sharing. This book will comprise high quality disseminations of new ideas, technology focus, research results and discussions on the evolution of Clinical and Medical image analysis techniques for the benefit of both scientific and industrial developments.
Features:
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- Research aspects in clinical and medical image processing
- Human Computer Interaction and interface in imaging diagnostics
- Intelligent Imaging Systems for effective analysis using machine learning algorithms
- Clinical and Scientific Evaluation of Imaging Studies
- Computer-aided disease detection and diagnosis
- Clinical evaluations of new technologies
- Mobility and assistive devices for challenged and elderly people
This book serves as a reference book for researchers and doctoral students in the clinical and medical imaging domain including radiologists. Industries that manufacture imaging modality systems and develop optical systems would be especially interested in the challenges and solutions provided in the book. Professionals and practitioners in the medical and clinical imaging may be benefited directly from authors’ experiences.
商品描述(中文翻譯)
《臨床與醫學影像的電腦化分析進展》一書致力於透過發表學術研究來傳播知識，主要集中於臨床與醫學影像領域。所涵蓋的章節類型包括基於幾何、統計、物理和功能的算法及策略的開發與實施，以解決以下類型的問題，使用醫學影像數據集：可視化、特徵提取、分割、影像引導手術、圖像數據的表示、統計形狀分析、計算生理學以及醫學影像的遠程醫療。
本書突顯了所有醫學與臨床影像研究者在臨床與醫學影像分析技術方面的基本進展。這本書將成為來自世界各地的醫學影像和臨床研究專業人士、傑出科學家和教育工作者的良好知識分享來源。本書將包含高品質的新想法、技術焦點、研究結果的傳播以及對臨床與醫學影像分析技術演變的討論，以促進科學和工業的發展。
特色：
- 臨床與醫學影像處理的研究面向
- 醫學影像診斷中的人機互動與介面
- 使用機器學習算法進行有效分析的智能影像系統
- 醫學影像研究的臨床與科學評估
- 電腦輔助的疾病檢測與診斷
- 新技術的臨床評估
- 針對身心障礙者和老年人的移動輔助設備
本書作為臨床與醫學影像領域研究人員和博士生的參考書，包括放射科醫生。製造影像模態系統和開發光學系統的行業將特別對書中提供的挑戰和解決方案感興趣。醫學與臨床影像的專業人士和從業者將直接受益於作者的經驗。
作者簡介
J. Dinesh Peter is currently working as Associate Professor, Department of Computer Science and Engineering at Karunya Institute of Technology & Sciences, Coimbatore. Prior to this, he was a full time research scholar at National Institute of Technology, Calicut, India, from where he received his PhD in computer science and engineering. His research focus includes Big-data, image processing and computer vision. He has several publications in various reputed international journals and conference papers which are widely referred to. He is a member of IEEE, MICCAI, Computer Society of India and Institution of Engineers India and has served as session chairs and delivered plenary speeches for various international conferences and workshops. He has conducted many international conferences and been as editor for Springer proceedings and many special issues in journals.
Steven Lawrence Fernandes is currently working as a postdoctoral researcher in the area of deep learning under the guidance of Professor Sumit Kumar Jha at The University of Central Florida, USA. He also has postdoctoral research experience working at The University of Alabama at Birmingham, USA. He has his Ph.D. in Computer Vision and Machine Learning from Karunya Institute of Technology & Sciences, Coimbatore, Tamil Nadu. His Ph.D work "Match Composite Sketch with Drone Images" has received patent notification (Patent Application Number: 2983/CHE/2015) from Government of India, Controller General of Patents, Designs & Trade Marks. He has received the prestigious US award from Society for Design and Process Science for his outstanding service contributions in the year 2017 and Young Scientist Award by Vision Group on Science and Technology, Government of Karnataka, India in the year 2014. He also received Research Grant from University of Houston Downtown, USA and The Institution of Engineers (India), Kolkata. He has collaborated with various Scientists, Professors, Researchers and jointly published more than 50 Research Articles which are in Science Citation Indexed (SCI) Journals.
Carlos E. Thomaz holds a degree in Electronic Engineering from the Pontifical Catholic University of Rio de Janeiro (1993), a Master's degree in Electrical Engineering from the Pontifical Catholic University of Rio de Janeiro (1999), a PhD and a postdoctoral degree in Computer Science - Imperial College London (2005). He is a full professor at FEI's University Center. He has experience in the area of Computer Science, with emphasis on Pattern Recognition in Statistics, working mainly in the following subjects: Computational Vision, Computation in Medical Images and Biometrics.
作者簡介(中文翻譯)
J. Dinesh Peter 目前擔任印度科印巴多爾 Karunya Institute of Technology & Sciences 計算機科學與工程系的副教授。在此之前，他曾是印度卡利庫特國立技術學院的全職研究學者，並在該校獲得計算機科學與工程的博士學位。他的研究重點包括大數據、影像處理和計算機視覺。他在多個知名國際期刊和會議論文中發表了多篇文章，並受到廣泛引用。他是 IEEE、MICCAI、印度計算機學會及印度工程師協會的成員，並曾擔任多個國際會議和研討會的會議主席及主題演講者。他主持過多個國際會議，並擔任 Springer 會議錄及多個期刊特刊的編輯。
Steven Lawrence Fernandes 目前在美國中央佛羅里達大學擔任深度學習領域的博士後研究員，指導教授為 Sumit Kumar Jha。他也曾在美國阿拉巴馬大學伯明翰分校擔任博士後研究員。他在印度科印巴多爾的 Karunya Institute of Technology & Sciences 獲得計算機視覺與機器學習的博士學位。他的博士論文「Match Composite Sketch with Drone Images」已獲得印度政府專利局的專利通知（專利申請號：2983/CHE/2015）。他於2017年獲得美國設計與過程科學學會頒發的卓越服務貢獻獎，並於2014年獲得印度卡納塔克邦政府科學與技術視覺小組的青年科學家獎。他還獲得了美國休斯頓市立大學及印度加爾各答工程師協會的研究資助。他與多位科學家、教授和研究人員合作，聯合發表了超過50篇在科學引文索引（SCI）期刊上的研究文章。
Carlos E. Thomaz 擁有里約熱內盧教宗天主教大學的電子工程學位（1993年）、里約熱內盧教宗天主教大學的電機工程碩士學位（1999年）、倫敦帝國學院的計算機科學博士學位及博士後學位（2005年）。他是 FEI 大學中心的全職教授。他在計算機科學領域擁有經驗，專注於統計中的模式識別，主要研究主題包括計算機視覺、醫學影像計算及生物識別技術。
目錄大綱
1. A New Biomarker for Alzheimer’s Based on the Hippocampus Image
Through the Evaluation of the Surface Charge Distribution
[Aldo A. Belardi, Fernandho de O. Freitas, Rodrigo P. Bechelli, and
Rodrigo G. G. Piva]
2. Independent Vector Analysis of Non-Negative Image Mixture Model
for Clinical Image Separation
[D. Sugumar, P. T. Vanathi, Xiao-Zhi Gao, Felix Erdmann Ott, and
M. S. Aezhisai Vallavi]
3. Rationalizing of Morphological Renal Parameters and eGFR for Chronic
Kidney Disease Detection
[Deepthy Mary Alex, D. Abraham Chandy, and Anand Paul]
4. Human Computer Interface for Neurodegenerative Patients Using
Machine Learning Algorithms
[S. Ramkumar, G. Emayavaramban, J. Macklin Abraham Navamani, R. Renuga Devi,
A. Prema, B. Booba, and P. Sriramakrishnan]
5. Smart Mobility System for Physically Challenged People
[S. Sundaramahalingam, B. V. Manikandan, K. Banumalar, and S. Arockiaraj]
6. DHS: The Cognitive Companion for Assisted Living of the Elderly
[R. Krithiga]
7. Raspberry Pi Based Cancer Cell Detection Using Segmentation Algorithm
[S. Yogashri, S. Jayanthy, and C. Narendhar]
8. An AAC Communication Device for Patients with Total Paralysis
[Oshin R. Jacob and Sundar G. Naveen]
9. Case Studies on Medical Diagnosis Using Soft Computing Techniques
[Mary X. Anitha, Lina Rose, Aldrin Karunaharan, and Anand Pushparaj J.]
10. Alzheimer’s Disease Classification Using Machine Learning Algorithms
[S. Naganandhini, P. Shanmugavadivu, A. Asaithambi, and M. Mohammed
Mansoor Roomi]
11. Fetal Standard Plane Detection in Freehand Ultrasound Using Multi
Layered Extreme Learning Machine
[S. Jayanthi Sree and C. Vasanthanayaki]
12. Earlier Prediction of Cardiovascular Disease Using IoT and Deep Learning
Approaches
[R. Sivaranjani and N. Yuvaraj]
13. Analysis of Heart Disease Prediction Using Various Machine Learning
Techniques
[M. Marimuthu, S. Deivarani, and R. Gayathri]
14. Computer-Aided Detection of Breast Cancer on Mammograms: Extreme
Learning Machine Neural Network Approach
[Jayesh George M. and Perumal Sankar S.]
15. Deep Learning Segmentation Techniques for Checking the Anomalies
of White Matter Hyperintensities in Alzheimer’s Patients
[Antonitta Eileen Pious and Sridevi Unni]
16. Investigations on Stabilization and Compression of Medical Videos
[D. Raveena Judie Dolly, D. J. Jagannath, and R. Anup Raveen Jaison]
17. An Automated Hybrid Methodology Using Firefly Based Fuzzy Clustering
for Demarcation of Tissue and Tumor Region in Magnetic Resonance
Brain Images
[Saravanan Alagarsamy, Kartheeban Kamatchi, and Vishnuvarthanan Govindaraj]
18. A Risk Assessment Model for Alzheimer’s Disease Using Fuzzy
Cognitive Map
[S. Meenakshi Ammal and L. S. Jayashree]
19. Comparative Analysis of Texture Patterns for the Detection of Breast
Cancer Using Mammogram Images
[J. Shiny Christobel and J. Joshan Athanesious]
20. Analysis of Various Color Models for Endoscopic Images
[Caren Babu, Anand Paul, and D. Abraham Chandy]
21. Adaptive Fractal Image Coding Using Differential Scheme for
Compressing Medical Images
[P. Chitra, M. Mary Shanthi Rani, and V. Sivakumar]
目錄大綱(中文翻譯)
1. A New Biomarker for Alzheimer’s Based on the Hippocampus Image
Through the Evaluation of the Surface Charge Distribution
[Aldo A. Belardi, Fernandho de O. Freitas, Rodrigo P. Bechelli, and
Rodrigo G. G. Piva]
2. Independent Vector Analysis of Non-Negative Image Mixture Model
for Clinical Image Separation
[D. Sugumar, P. T. Vanathi, Xiao-Zhi Gao, Felix Erdmann Ott, and
M. S. Aezhisai Vallavi]
3. Rationalizing of Morphological Renal Parameters and eGFR for Chronic
Kidney Disease Detection
[Deepthy Mary Alex, D. Abraham Chandy, and Anand Paul]
4. Human Computer Interface for Neurodegenerative Patients Using
Machine Learning Algorithms
[S. Ramkumar, G. Emayavaramban, J. Macklin Abraham Navamani, R. Renuga Devi,
A. Prema, B. Booba, and P. Sriramakrishnan]
5. Smart Mobility System for Physically Challenged People
[S. Sundaramahalingam, B. V. Manikandan, K. Banumalar, and S. Arockiaraj]
6. DHS: The Cognitive Companion for Assisted Living of the Elderly
[R. Krithiga]
7. Raspberry Pi Based Cancer Cell Detection Using Segmentation Algorithm
[S. Yogashri, S. Jayanthy, and C. Narendhar]
8. An AAC Communication Device for Patients with Total Paralysis
[Oshin R. Jacob and Sundar G. Naveen]
9. Case Studies on Medical Diagnosis Using Soft Computing Techniques
[Mary X. Anitha, Lina Rose, Aldrin Karunaharan, and Anand Pushparaj J.]
10. Alzheimer’s Disease Classification Using Machine Learning Algorithms
[S. Naganandhini, P. Shanmugavadivu, A. Asaithambi, and M. Mohammed
Mansoor Roomi]
11. Fetal Standard Plane Detection in Freehand Ultrasound Using Multi
Layered Extreme Learning Machine
[S. Jayanthi Sree and C. Vasanthanayaki]
12. Earlier Prediction of Cardiovascular Disease Using IoT and Deep Learning
Approaches
[R. Sivaranjani and N. Yuvaraj]
13. Analysis of Heart Disease Prediction Using Various Machine Learning
Techniques
[M. Marimuthu, S. Deivarani, and R. Gayathri]
14. Computer-Aided Detection of Breast Cancer on Mammograms: Extreme
Learning Machine Neural Network Approach
[Jayesh George M. and Perumal Sankar S.]
15. Deep Learning Segmentation Techniques for Checking the Anomalies
of White Matter Hyperintensities in Alzheimer’s Patients
[Antonitta Eileen Pious and Sridevi Unni]
16. Investigations on Stabilization and Compression of Medical Videos
[D. Raveena Judie Dolly, D. J. Jagannath, and R. Anup Raveen Jaison]
17. An Automated Hybrid Methodology Using Firefly Based Fuzzy Clustering
for Demarcation of Tissue and Tumor Region in Magnetic Resonance
Brain Images
[Saravanan Alagarsamy, Kartheeban Kamatchi, and Vishnuvarthanan Govindaraj]
18. A Risk Assessment Model for Alzheimer’s Disease Using Fuzzy
Cognitive Map
[S. Meenakshi Ammal and L. S. Jayashree]
19. Comparative Analysis of Texture Patterns for the Detection of Breast
Cancer Using Mammogram Images
[J. Shiny Christobel and J. Joshan Athanesious]
20. Analysis of Various Color Models for Endoscopic Images
[Caren Babu, Anand Paul, and D. Abraham Chandy]
21. Adaptive Fractal Image Coding Using Differential Scheme for
Compressing Medical Images
[P. Chitra, M. Mary Shanthi Rani, and V. Sivakumar]
