Computational Intelligence in Medical Imaging Techniques and Applications

CI Techniques & Algorithms for a Variety of Medical Imaging SituationsDocuments 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.

Preface. Computational Intelligence on Medical Imaging with Artificial Neural Networks. Evolutionary Computing and Its Use in Medical Imaging. Rough Sets in Medical Imaging: Foundations and Trends. Early Detection of Wound Inflammation by Color Analysis. Analysis and Applications of Neural Networks for Skin Lesion Border Detection. Prostate Cancer Classification Using Multispectral Imagery and Metaheuristics. Intuitionistic Fuzzy Processing of Mammographic Images. Fuzzy C-Means and Its Applications in Medical Imaging. Image Informatics for Clinical and Preclinical Biomedical Analysis. Parts-Based Appearance Modeling of Medical Imagery. Reinforced Medical Image Segmentation. Image Segmentation and Parameterization for Automatic Diagnostics of Whole-Body Scintigrams: Basic Concepts. Distributed 3-D Medical Image Registration Using Intelligent Agents. Monte Carlo-Based Image Reconstruction in Emission Tomography. Deformable Organisms: An Artificial Life Framework for Automated Medical Image Analysis. Index.