Medical Imaging Systems Techniques And Applications Computational Techniques

The field of medical imaging has been revolutionized by new techniques in powerful computations, image processing, and modalities such as Computer-Aided Tomography (CAT) and Magnetic Resonance Imaging (MRI), among others.This book clearly reveals the effectiveness and great significance of the modalities available,such as Computer-Aided Tomography (CAT) and Magnetic Resonance Imaging (MRI), among others.

First Published in 2004. Routledge is an imprint of Taylor & Francis, an informa company.

First Published in 2004. This is Volume I of six of a series on medical imaging systems techniques and applications. This subject area exemplifies a meaningful manifestation of the power of the technologies of the second industrial revolution. The first chapter in this volume on cardiovascular systems emphasizes the importance of accurate measurements of cardiac shape and dynamics as they reflect the scope of cardiac diseases, the major cause of mortality in developed countries today. Cardiac imaging plays an important role in this regard, and almost the only one in this clinical context.

Readership: Academics, researchers, industrialists, postgraduate and graduate students in databases, fuzzy logic, machine vision/pattern recognition, neural networks, bioengineering, electrical & electronic engineering, and bioinformatics.Key Features: Provides a significant and uniquely comprehensive reference source for research workers and practitioners Features 130 contributors from 27 countries, among the foremost authorities in industry, government and academia Institutions, laboratories and individuals involved in the area of medical imaging should possess this setKeywords:Medical Imaging;Systems Technology;Cardiovascular Systems;Brain Systems;General Anatomy;Modalities;Diagnosis Optimization Methods;Computational Methods

Ch. 1. Modeling for medical image analysis : framework and applications / Marek Kretowski and Johanne Bézy-Wendling -- ch. 2. Biomechanical models for image analysis and simulation / M. Sermesant, H. Delingette and N. Ayache -- ch. 3. Techniques in fractal analysis and their applications in brain MRI / Khan M. Iftekharuddin -- ch. 4. Techniques in infrared microspectroscopy and advanced computational methods for colon cancer diagnosis / S. Mordechai ... [et al.] -- ch. 5. Advances in computerized image analysis methods on breast ultrasound / Anant Madabhushi and Dimitris N. Metaxas -- ch. 6. Techniques in blind deblurring of spiral computed tomography images and their applications / Ming Jiang and Jing Wang -- ch. 7. Model-based 3D encoding/2D decoding of medical imaging data / G. Menegaz -- ch. 8. Interpolation techniques in multimodality image registration and their application / Jeffrey Tsao, Jim Xiuquan Ji and Zhi-Pei Liang -- ch. 9. Automatic construction of cardiac statistical shape models : applications in SPECT and MR imaging / Sebastián Ordás and Alejandro F. Frangi -- ch. 10. Techniques for mutual information-based brain image. Registration and their applications / Hua-Mei Chen and Pramod K. Varshney -- ch. 11. Iterative algebraic algorithms for image reconstruction / Ming Jiang

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.