Computational Intelligence in Medical Imaging
Techniques and Applications
CI Techniques & Algorithms for a Variety of Medical Imaging Situations
Documents recent advances and stimulates further research
A compilation of the latest trends in the field, Computational Intelligence in Medical Imaging: Techniques and Applications explores how intelligent computing can bring enormous benefit to existing technology in medical image processing as well as improve medical imaging research. The contributors also cover state-of-the-art research toward integrating medical image processing with artificial intelligence and machine learning approaches.
The book presents numerous techniques, algorithms, and models. It describes neural networks, evolutionary optimization techniques, rough sets, support vector machines, tabu search, fuzzy logic, a Bayesian probabilistic framework, a statistical parts-based appearance model, a reinforcement learning-based multistage image segmentation algorithm, a machine learning approach, Monte Carlo simulations, and intelligent, deformable models. The contributors discuss how these techniques are used to classify wound images, extract the boundaries of skin lesions, analyze prostate cancer, handle the inherent uncertainties in mammographic images, and encapsulate the natural intersubject anatomical variance in medical images. They also examine prostate segmentation in transrectal ultrasound images, automatic segmentation and diagnosis of bone scintigraphy, 3-D medical image segmentation, and the reconstruction of SPECT and PET tomographic images.
Table of Contents
Computational Intelligence on Medical Imaging with Artificial Neural Networks, Z.Q. Wu, Jianmin Jiang, and Y.H. Peng
Evolutionary Computing and Its Use in Medical Imaging, Lars Nolle and Gerald Schaefer
Rough Sets in Medical Imaging: Foundations and Trends, Aboul Ella Hassanien, Ajith Abraham, James F. Peters, and Janusz Kacprzyk
Early Detection of Wound Inflammation by Color Analysis, Peter Plassmann and Brahima Belem
Analysis and Applications of Neural Networks for Skin Lesion Border Detection, Maher I. Rajab
Prostate Cancer Classification Using Multispectral Imagery and Metaheuristics, Muhammad Atif Tahir, Ahmed Bouridane, and Muhammad Ali Roula
Intuitionistic Fuzzy Processing of Mammographic Images, Ioannis K. Vlachos and George D. Sergiadis
Fuzzy C-Means and Its Applications in Medical Imaging, Huiyu Zhou
Image Informatics for Clinical and Preclinical Biomedical Analysis, Kenneth W. Tobin, Edward Chaum, Jens Gregor, Thomas P. Karnowski, Jeffery R. Price, and Jonathan Wall
Parts-Based Appearance Modeling of Medical Imagery, Matthew Toews and Tal Arbel
Reinforced Medical Image Segmentation, Farhang Sahba, Hamid R. Tizhoosh, and Magdy M.A. Salama
Image Segmentation and Parameterization for Automatic Diagnostics of Whole-Body Scintigrams: Basic Concepts, Luka Šajn and Igor Kononenko
Distributed 3-D Medical Image Registration Using Intelligent Agents, Roger J. Tait, Gerald Schaefer, and Adrian A. Hopgood
Monte Carlo-Based Image Reconstruction in Emission Tomography, Steven Staelens and Ignace Lemahieu
Deformable Organisms: An Artificial Life Framework for Automated Medical Image Analysis, Ghassan Hamarneh, Chris McIntosh, Tim McInerney, and Demetri Terzopoulos
In choosing this book the reader will be exposed to the range of exciting research that is being conducted in the context of medical imaging. … I am sure that this collection of the latest trends and developments will further stimulate discussion and development of new solutions. The book will be of interest and relevance to anyone involved in the computational analysis and interpretation of images—whether medical or not.
—International Statistical Review, 2009