Statistics of Medical Imaging (Chapman & Hall/CRC Interdisciplinary Statistics)
By: Tianhu Lei (author)Hardback
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Statistical investigation into technology not only provides a better understanding of the intrinsic features of the technology (analysis), but also leads to an improved design of the technology (synthesis). Physical principles and mathematical procedures of medical imaging technologies have been extensively studied during past decades. However, less work has been done on the statistical aspects of these techniques. Statistics of Medical Imaging fills this gap and provides a theoretical framework for statistical investigation into medical imaging technologies.
Describes physical principles and mathematical procedures of two medical imaging techniques: X-ray CT and MRIPresents statistical properties of imaging data (measurements) at each stage in the imaging processes of X-ray CT and MRIDemonstrates image reconstruction as a transform from a set of random variables (imaging data) to another set of random variables (image data)Presents statistical properties of image data (pixel intensities) at three levels: a single pixel, any two pixels, and a group of pixels (a region)Provides two stochastic models for X-ray CT and MR image in terms of their statistics and two model-based statistical image analysis methodsEvaluates statistical image analysis methods in terms of their detection, estimation, and classification performancesIndicates that X-ray CT, MRI, PET and SPECT belong to a category of imaging: the non-diffraction computed tomography
Rather than offering detailed descriptions of statistics of basic imaging protocols of X-ray CT and MRI, this book provides a method to conduct similar statistical investigations into more complicated imaging protocols.
Tianhu Lei is an associate professor at the University of Pittsburgh. He has previously worked at the University of Maryland, the University of Pennsylvania, and the Children's Hospital of Philadelphia. He earned a Ph.D. in electric and system engineering from the University of Pennsylvania.
IntroductionData Flow and StatisticsImaging and Image StatisticsStatistical Image AnalysisMotivation and OrganizationX-ray CT Physics and MathematicsIntroductionPhoton Emission, Attenuation, and DetectionAttenuation CoefficientProjectionsMathematical Foundation of Image ReconstructionFourier Slice theoremImage ReconstructionMRI Physics and MathematicsIntroductionNuclear Spin and Magnetic MomentAlignment and PrecessionMacroscopic MagnetizationResonance and RelaxationBloch Equation and Its SolutionExcitationInductionk-Space and k-Space SampleImage ReconstructionEcho SignalNon-diffraction Computed TomographyIntroductionInteraction between EM Wave and ObjectInverse Scattering ProblemNon-diffraction Computed TomographyStatistics of X-ray CT ImagingIntroductionStatistics of Photon MeasurementsStatistics of ProjectionsStatistical Interpretation of X-ray CT Image ReconstructionStatistics of X-ray CT ImageIntroductionStatistics of the Intensity of a Single PixelStatistics of the Intensities of Two PixelsStatistics of the Intensities of a Group of PixelsStatistics of MR ImagingIntroductionStatistics of Macroscopic MagnetizationsStatistics of MR SignalsStatistics of k-Space SamplesStatistical Interpretation of MR Image ReconstructionStatistics of MR ImageIntroductionStatistics of the Intensity of a Single PixelStatistics of the Intensities of Two PixelsStatistics of the Intensities of a Group of PixelsDiscussion and RemarksStochastic Image ModelsIntroductionStochastic Model IStochastic Model IIDiscussion Statistical Image Analysis - IIntroductionDetection of Number of Image RegionsEstimation of Image ParametersClassification of PixelsStatistical Image AnalysisStatistical Image Analysis - IIIntroductionDetection of the Number of Image RegionsEstimation of Image ParametersClassification of PixelsStatistical Image AnalysisPerformance Evaluation of Image Analysis MethodsIntroductionPerformance of the iFNM Model-Based Image Analysis MethodPerformance of the cFNM Model-Based Image Analysis MethodIndex
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