Computer-Aided Intelligent Diagnosis of Skin Melanoma

Erscheinungsjahr 2025
ISBN: 978-981-966134-3
Verlag: Springer Nature Singapore

Buch, Englisch, 132 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 426 g

This book is intended primarily for researchers, engineers, and graduate students in the fields of medicine management and software engineering in the field of dermatology.

Aiming at the problems of color deviation, structured noise interference, and uneven distribution of feature points in the identification of melanoma, this book researches four aspects of dermoscopic image such as color correction, hair removal, image registration and classification recognition. Some creative methods are presented as follows, 1 Dermoscopic image color correction, 2 Dermoscopic image hair removal, 3 Dermoscopic image registration. In writing style, a combination of diagrams, tables, formula and text is used in order that the book can be understood easily and raise the readers’ interest, and in content, theoretical analysis and medical application are combined to describe the advanced technologies in every chapter.

Zielgruppe

Research

Autoren/Hrsg.

Guo, Shuli

Han, Lina

Song, Xiaowei

Fachgebiete

• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe
• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung

Inhaltsverzeichnis

Introduction.- Color correction based on gamma correction and multi-scale image fusion.- Hair removal from dermoscopic images based on maximum variance fuzzy clustering and improved Criminisi algorithm.- Hair removal from dermoscopic images based on texture spectrum iterative tensor voting and optimized total variation algorithm.- Dermoscopic image registration based on adaptive SURF feature extraction and weighted nuclear norm minimization fusion.

Über Autor(innen)

Dr. Shuli Guo , the full Professor of the Beijing Institute of Technology, Beijing China. He was born in 1973 in Inner Mongolia, P.R. China. He received his Bachelor Degree (1995), Master Degree (1998) from Inner Mongolia Normal University, and his Ph.D. Degree from Peking University (2001). Dr. Guo worked as a postdoctoral researcher from Jan. 2002 to Apr. 2004 at Tsinghua University, as a researcher fellow from January 2008 to September 2008 at Akita Prefectural University, and as a postdoctoral researcher from September 2008 to September 2009 at Oxford University.

Prof.Guo is interested in the following fields such as classical control theory and application, medical image processing, medical data analysis, typical chronic disease analysis and remote medical monitoring. Recently, he has published nearly 70 impactful papers, 5 monorgraphs, 36 authorized Chinese patents, 18 authorized software copyrights, and he has published 7 Chinese national standards and 5 electronic industrial standards. Simultaneously, he obtained one third prize for military medical achievements, two third prize of science and technology of the Chinese Medical Association and one third prize of Beijing Medical Science and Technology. More than 60 post-graduate students from global have graduated, and he is the member of Chinese National Technical Committee for Professional Standardization (2021-2026) and the head of China Geriatric Disease Standardized Diagnosis and Treatment Society of CAGG(2023-2028) , and the chief expert of Chinese National Key Research and Development Program(2017-2021).

Dr. Song Xiaowei, was born in Shanxi Province, China in 1993. He received his bachelor's degree from Taiyuan University of Science and Technology(2016), his master's degree from Hebei University of Technology(2019), and his doctorate degree from Beijing Institute of Technology in 2024. He has been a lecturer at North University of China since 2024. His research interests include intelligent recognition of dermoscopic images, deep learning and machine learning methods.

Prof. Lina Han was born in 1973 in Jilin Province, P.R. China. She received her Med. Bachelor Degree (1995), Med. Master Degree (2000), and Ph.D. Degree (2003) from Jilin University. Dr. Han worked as a postdoctoral researcher from July 2003 to April 2005 at Chinese PLA General Hospital, and as a research fellow from August 2008 to September 2009 at Kyoto University. And now, she works as a research fellow in Department of Cardiovascular Internal Medicine, The 2rd Medicine Centre of Chinese PLA General Hospital and Chinese PLA Medical School. Her research interests focus on digital and intelligent management on chronic diseases and comorbidities in the elderly and their medical solutions.