The book contains the contributions at the NATO Study Institute on Exposure and Risk Assessment of Chemical Pollution - Contemporary Methodology, which took place in Sofia - Borovetz, Bulgaria, July 1-10, 2008. Rapid advances in mathematics, computer science and molecular biology and chemistry have lead to the development in of a new branch of toxicology called Computational Toxicology. This emerging field is addressing the estimation and prediction of exposure risk and effects of chemicals based on experimental data, measured concentration and biological mechanisms and computational models of biological systems. Mathematical models are also being used to predict the fate and transport of substances in the environment. Because this area is still in its infancy, there has been limited application from governmental agencies to regulating controllable processes, such as registration of new chemicals, determination of estimated exposure and risk based limits and maximum acceptable concentrations in different compartments of the environment - ambient air, waters, soil and food products. However, this is soon to change as the ability to collect, analyze and interpret the required information is becoming increasingly more efficient and cost effective. Full implementation of the new processes have to involve education on both part of the experimentalists who are generating the data and the models, and the risk assessors who will use them to better protect human health and the environment.

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.

The book contains the contributions at the NATO Study Institute on Exposure and Risk Assessment of Chemical Pollution - Contemporary Methodology, which took place in Sofia - Borovetz, Bulgaria, July 1-10, 2008. Rapid advances in mathematics, computer science and molecular biology and chemistry have lead to the development in of a new branch of toxicology called Computational Toxicology. This emerging field is addressing the estimation and prediction of exposure risk and effects of chemicals based on experimental data, measured concentration and biological mechanisms and computational models of biological systems. Mathematical models are also being used to predict the fate and transport of substances in the environment. Because this area is still in its infancy, there has been limited application from governmental agencies to regulating controllable processes, such as registration of new chemicals, determination of estimated exposure and risk based limits and maximum acceptable concentrations in different compartments of the environment - ambient air, waters, soil and food products. However, this is soon to change as the ability to collect, analyze and interpret the required information is becoming increasingly more efficient and cost effective. Full implementation of the new processes have to involve education on both part of the experimentalists who are generating the data and the models, and the risk assessors who will use them to better protect human health and the environment.

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.