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Medical Image Computing and Computer Assisted Intervention – MICCAI 2018
The four-volume set LNCS 11070, 11071, 11072, and 11073 constitutes the refereed proceedings of the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018, held in Granada, Spain, in September 2018. The 373 revised full papers presented were carefully reviewed and selected from 1068 submissions in a double-blind review process. The papers have been organized in the following topical sections: Part I: Image Quality and Artefacts; Image Reconstruction Methods; Machine Learning in Medical Imaging; Statistical Analysis for Medical Imaging; Image Registration Methods. Part II: Optical and Histology Applications: Optical Imaging Applications; Histo...
Medical Imaging and Computer-Aided Diagnosis
This book covers virtually all aspects of image formation in medical imaging, including systems based on ionizing radiation (x-rays, gamma rays) and non-ionizing techniques (ultrasound, optical, thermal, magnetic resonance, and magnetic particle imaging) alike. In addition, it discusses the development and application of computer-aided detection and diagnosis (CAD) systems in medical imaging. Given its coverage, the book provides both a forum and valuable resource for researchers involved in image formation, experimental methods, image performance, segmentation, pattern recognition, feature extraction, classifier design, machine learning / deep learning, radiomics, CAD workstation design, human–computer interaction, databases, and performance evaluation.
8th International Conference on Bioinformatics and Biomedical Engineering (iCBBE)
It is my great pleasure to present the proceedings of the 8th International Conference on Bioinformatics and Biomedical Engineering (ICBBE 2014), held in Suzhou, China, September 20–22, 2014. I would like to take this opportunity to express my sincere thanks to all the authors and participants for their support to our conference. The continuous researches on Bioinformatics and Biomedical Engineering are now of critical significance to the sustainable development of science, education, culture and the society. Especially in modern times, it plays an important role in the interdisciplinary field among the life science, mathematical science, computer science and electronic information science...
Information Processing in Medical Imaging
This book constitutes the refereed proceedings of the 22nd International Conference on Information Processing in Medical Imaging, IPMI 2011, held at Kloster Irsee, Germany, in July 2011. The 24 full papers and 39 poster papers included in this volume were carefully reviewed and selected from 224 submissions. The papers are organized in topical sections on segmentation, statistical methods, shape analysis, registration, diffusion imaging, disease progression modeling, and computer aided diagnosis. The poster sessions deal with segmentation, shape analysis, statistical methods, image reconstruction, microscopic image analysis, computer aided diagnosis, diffusion imaging, functional brain analysis, registration and other related topics.
Biophysical Methods for Diagnosing Human Tissue Anomalies
Medical diagnosis of tissue anomalies, particularly cancer, is often limited by the constraints of current imaging technologies. This book introduces two approaches to address this issue: the imaging and the non-imaging methods. In the imaging category, the book unveils a pioneering technique based on radio tomosynthesis. Initially proven effective in detecting breast anomalies, this imaging method is now under evaluation for its potential in identifying brain anomalies. For non-imaging diagnostics, it delves into Fourier-transform infrared spectroscopy (FTIR), a technique known for its speed and reliability. The book demonstrates its successful application in diagnosing a range of cancers, including oral, uterine, ovarian, gastrointestinal, colorectal, and skin cancers. Furthermore, it explores its utility in predicting embryo quality and assessing pressure injuries. To augment these methods, the book employs machine learning algorithms, evaluating their efficacy in creating discriminative models for tissue anomalies.
