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"ntroductionTypical Measurement Systems Sources of Variability: Noise Analog Filters: Filter Basics Analog-to-Digital Conversion: Basic Concepts Time Sampling: Basics Data Banks Problems Basic Concepts Noise Data Functions and Transforms Convolution, Correlation, and Covariance Sampling Theory and Finite Data Considerations Problems Spectral Analysis: Classical Methods Introduction The Fourier Transform: Fourier Series Analysis Aperiodic Functions MATLAB Implementation: Direct FFT Truncated Fourier Analysis: Data Windowing MATLAB Implementation: Window Functions Power Spectrum MATLAB Implement."

"Applied Medical Image Processing: A Basic Course introduces the basic methods in applied image processing without assuming that readers have extensive prior knowledge beyond basic applied mathematics, physics, and programming. Illustrated with simple, well-commented MATLAB® examples, the book’s tangible and accessible presentation demonstrates real-life applications.The rapid evolution of radiological imaging in the past four decades has brought medical image processing into the forefront as an essential tool for clinical research, a crucial component of modern diagnostics, and an indispensable element the actual treatment of diseases. A hands-on introduction to the basic image processing algorithms used in clinical routine applications, Applied Medical Image Processing: A Basic Course closes the gap between basic engineering knowledge such as simple programming and applied mathematics on the one hand, and the general understanding of a science that affects the health of a broad public."

"Applied Medical Image Processing: A Basic Course introduces the basic methods in applied image processing without assuming that readers have extensive prior knowledge beyond basic applied mathematics, physics, and programming. Illustrated with simple, well-commented MATLAB® examples, the book’s tangible and accessible presentation demonstrates real-life applications.The rapid evolution of radiological imaging in the past four decades has brought medical image processing into the forefront as an essential tool for clinical research, a crucial component of modern diagnostics, and an indispensable element the actual treatment of diseases. A hands-on introduction to the basic image processing algorithms used in clinical routine applications, Applied Medical Image Processing: A Basic Course closes the gap between basic engineering knowledge such as simple programming and applied mathematics on the one hand, and the general understanding of a science that affects the health of a broad public."

"Creating new medical ontologies for image annotation focuses on the problem of the medical images automatic annotation process, which is solved in an original manner by the authors. All the steps of this process are described in detail with algorithms, experiments and results. In addition, the authors treat the problem of creating ontologies in an automatic way, starting from Medical Subject Headings (MESH). They have presented some efficient and relevant annotation models and also the basics of the annotation model used by the proposed system, cross media relevance models. Based on a text query the system will retrieve the images that contain objects described by the keywords."