Signal Processing and Performance Analysis for Imaging Systems

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An introduction to the application of digital signal processing to practical problems involving biomedical signals and systems. Topic include: examples of biomedical signals; analysis of concurrent, coupled, and correlated processes; filtering for removal of artifacts; event detection, analysis of waveshape and waveform complexity; frequency domain characterization of signals and systems; modeling biomedical signal-generating processes and systems; analysis of nonstationary signals; pattern classification and diagnostic decision.

MATLAB will be used throughout to provide numerous opportunities for hands-on application of the theory and techniques discussed to real-life biomedical signals. This course covers basic principles and modes of bioimaging methods for biomedical sciences. Topics include interaction of electromagnetic radiation with tissue, basic concepts in imaging and detection, basic modes of imaging modalities e. Model systems to be used to teach the topics include conventional imaging modalities such as optical imaging, optical microscopy, X-ray, computed tomography, ultrasound, magnetic resonance imaging, etc.

This course also includes hands-on exercise that reinforces important concepts. This course sets the foundation of computational neuroscience: a branch of neuroscience that creates computable models of biological neural systems, in particular large scale neural networks for processing sensory information. The course builds on basic neural modeling, presents computable neuron models and extends to large networks of neurons.

Additionally, the course is deeply rooted in machine learning, and supervised and unsupervised learning systems. The application will be centered in synthetic and artificial vision and audition, perception, intelligence for robots and automatic systems. Lecture will include an overview of the state-of-the-art in the field, new opportunities and ideas for innovation and success with such systems.

Systems-level lectures will be in the form of recent paper review and discussion. An introduction to the field of biosensors and an in-depth and quantitative view of device design and performance analysis.

An overview of the current state of the art to enable continuation into advanced biosensor work and design. Topics emphasize biomedical, bioprocessing, environmental, food safety, and biosecurity applications. This course focuses on the principles and applications of various established and emerging technologies for imaging brain activity in vivo across a wide range of spatial and temporal scales.

It covers functional magnetic resonance imaging, positron emission tomography, single-photon emission computed tomography, electroencephalography, magnetoencephalography, diffuse optical tomography, intrinsic signal optical imaging, voltage sensitive dye imaging, two-photo calcium imaging, functional ultrasound, and photoacoustic tomography, all in the context of brain functions.

Special emphasis is on the pros and cons of individual modalities, and their integration toward more comprehensive understandings of how human sensation, behavior, and cognition emerge from complex network activity. Image Resampling. Super-resolution Image Reconstruction. Deblur Filtering. Image Contrast Enhancement. Non-Uniformity Correction. Tone Scale.

Image Fusion. Resolution and Sensitivity.

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Linear Shift Invariant Imaging Systems. Sampled Imaging System.

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Signal to Noise Ratio. Electro-Optical and Infrared Imaging Systems. Threshold Vision. Image Quality Metric. The Fourier Transform. Fourier-based Filters. The Wavelet Transform. Image Display, Reconstruction, and Resampling. Sampling Theory and Sampling Artifacts.

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Image Resampling using Spatial Domain Methods. Alias-free Image Resampling using Fourier-based Methods. Image Resampling Performance Measurements.

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Super-Resolution Image Restoration. Super-Resolution Image Reconstruction. Super-Resolution Image Performance Measurements. Sensors that Benefit from Super-Resolution Reconstruction. All terms mentioned in this book that are known to be trademarks or service marks havebeen appropriately capitalized.

Signal Processing and Performance Analysis for Imaging Systems

Artech House cannot attest to the accuracy of this informa-tion. Use of a term in this book should not be regarded as affecting the validity of any trade-mark or service mark. PrefaceIn todays consumer electronics market where a 5-megapixel camera is no longerconsidered state-of-the-art, signal and image processing algorithms are real-timeand widely used.

They stabilize images, provide super-resolution, adjust for detec-tor nonuniformities, reduce noise and blur, and generally improve camera perfor-mance for those of us who are not professional photographers. Most of these signaland image processing techniques are company proprietary and the details of thesetechniques are never revealed to outside scientists and engineers. In addition, it isnot necessary for the performance of these systems including the algorithms to bedetermined since the metric of success is whether the consumer likes the productand buys the device.

In other imaging communities such as military imaging systems which, at aminimum, include visible, image intensifiers, and infrared and medical imagingdevices, it is extremely important to determine the performance of the imaging sys-tem, including the signal and image processing techniques.

In medical systems, the imaging system performancedetermines how accurately a diagnosis can be provided. Signal and image process-ing plays a key role in the performance of these imaging systems and, in the past 5 to10 years, has become a key contributor to increased imaging system performance. There is a great deal of government funding in signal and image processing forimaging system performance and the literature is full of university and governmentlaboratory developed algorithms.

There are still a great number of industry algo-rithms that, overall, are considered company proprietary. We focus on those in theliterature and those algorithms that can be generalized in a nonproprietary manner. There are numerous books in the literature on signal and image processing tech-niques, algorithms, and methods.

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The majority of these books emphasize the math-ematics of image processing and how they are applied to image information. Veryfew of the books address the overall imaging system performance when signal andimage processing is considered a component of the imaging system. Likewise, thereare many books in the area of imaging system performance that consider the optics,the detector, and the displays in the system and how the system performancebehaves with changes or modifications of these components.