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Conference on Medical imaging - Physics of Medical imaging

作者： Wells, Jered R. Dobbins, James T., iii Duke Univ Med Ctr Carl E Ravin Adv Imaging Labs Durham NC 27705 USA

ISBN: (纸本)9780819494429

Assessment of the resolution properties of nonlinear imaging systems is a useful but challenging task. While the modulation transfer function (MTF) fully describes contrast resolution as a function of spatial frequency for linear systems, an equivalent metric does not exist for systems with significant nonlinearity. Therefore, this preliminary investigation attempts to classify and quantify the amount of scaling and distortion imposed on a given image signal as the result of a nonlinear process (nonlinear image processing algorithm). As a proof-of-concept, a median filter is assessed in terms of its principle frequency response (PFR) and distortion response (DR) functions. These metrics are derived in frequency space using a sinusoidal basis function, and it is shown that, for a narrow-band sinusoidal input signal, the scaling and distortion properties of the nonlinear filter are described exactly by PFR and DR, respectively. The use of matched sinusoidal basis and input functions accurately reveals the frequency response to long linear structures of different scale. However, when more complex (multi-band) input signals are considered, PFR and DR fail to adequately characterize the frequency response due to nonlinear interaction effects between different frequency components during processing. Overall, the results reveal the context-dependent nature of nonlinear image processing algorithm performance, and they emphasize the importance of the basis function choice in algorithm assessment. In the future, more complex forms of nonlinear systems analysis may be necessary to fully characterize the frequency response properties of nonlinear algorithms in a context-dependent manner

关键词： nonlinear algorithms frequency response image processing metrology

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Signal processing for Sparse Discrete Time systems

Signal Processing for Sparse Discrete Time Systems

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作者： Taheri, Omid University of Alberta (Canada)

学位级别：Ph.D.

In recent years compressive sampling (CS) has appeared in the signal processing literature as a legitimate contender for processing of sparse signals. Natural signals such as speech, image and video are compressible. In most signal processing systems dealing with these signals the signal is first sampled and later on compressed. The philosophy of CS however is to sample and compress the signal at the same time. CS is finding applications in a wide variety of areas including medical imaging, seismology, cognitive radio, and channel estimation among others. Although CS has been given a great deal of attention in the past few years the theory is still naive and its fullest potential is still to be proven. The research in CS covers a wide span from theory of sampling and recovery algorithms to sampling device design to sparse CS-based signal processing applications. The contributions of this thesis are as follows; (i) The analog-to-information converter (AIC) is the device that is designed to collect compressed samples. It is a replacement for the analog-to-digital converter in a traditional signal processing system. We propose a modified structure for the AIC which leads to reducing the complexity of the current design without sacrificing the recovery performance. (ii) Traditional parameter estimation algorithms such as least mean square (LMS) do not assume any structural information about the system. Motivated by the ideas from CS we introduce a number of modified LMS algorithms for the sparse channel estimation problem. Decimated LMS algorithms for the special case of frequency sparse channels are also given. (iii) At last we consider the problem of CS of two dimensional signals. The most straightforward approach is to first find the vector form of a two dimensional signal and then use traditional CS methods to collect the compressed samples. However, our approach samples all the columns of a two dimensional signal with the same measurement matrix. This leads to simp

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Signal processing for Sparse Discrete Time systems.

Signal Processing for Sparse Discrete Time Systems.

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作者： Taheri,Omid. University of Alberta

学位级别：Doctor

In recent years compressive sampling CS) has appeared in the signal processing literature as a legitimate contender for processing of sparse signals. Natural signals such as speech,image and video are compressible. In most signal processing systems dealing with these signals the signal is first sampled and later on compressed. The philosophy of CS however is to sample and compress the signal at the same time. CS is finding applications in a wide variety of areas including medical imaging,seismology,cognitive radio,and channel estimation among others. Although CS has been given a great deal of attention in the past few years the theory is still naive and its fullest potential is still to be proven. The research in CS covers a wide span from theory of sampling and recovery algorithms to sampling device design to sparse CS-based signal processing applications. The contributions of this thesis are as follows； i) The analog-to-information converter AIC) is the device that is designed to collect compressed samples. It is a replacement for the analog-to-digital converter in a traditional signal processing system. We propose a modified structure for the AIC which leads to reducing the complexity of the current design without sacrificing the recovery performance. ii) Traditional parameter estimation algorithms such as least mean square LMS) do not assume any structural information about the system. Motivated by the ideas from CS we introduce a number of modified LMS algorithms for the sparse channel estimation problem. Decimated LMS algorithms for the special case of frequency sparse channels are also given. iii) At last we consider the problem of CS of two dimensional signals. The most straightforward approach is to first find the vector form of a two dimensional signal and then use traditional CS methods to collect the compressed samples. However,our approach samples all the columns of a two dimensional signal with the same measurement matrix. This leads to simplification

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Storm System Database: A Big Data Approach to Moving Object Databases

Storm System Database: A Big Data Approach to Moving Object ...

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4th International Conference on Computing for Geospatial Research and Application (COM.Geo)

作者： Olsen, Brian McKenney, Mark So Illinois Univ Dept Comp Sci Edwardsville IL 62026 USA

ISBN: (纸本)9780769550121

Rainfall data is often collected by measuring the amount of precipitation collected in a physical container at a site. Such methods provide precise data for those sites, but are limited in granularity to the number and placement of collection devices. We use radar images of storm systems that are publicly available and provide rainfall estimates for large regions of the globe, but at the cost of loss of precision. We present a moving object database called Storm DB that stores decibel measurements of rain clouds as moving regions, ie., we store a single rain cloud as a region that changes shape and position over time. Storm DB is a prototype system that answers rain amount queries over a user defined time duration for any point in the continental United States. In other words, a user can ask the database for the amount of rainfall that fell at any point in the US over a specified time window. Although this single query seems straightforward, it is complicated due to the expected size of the dataset: storm clouds are numerous, radar images are available in high resolution, and our system will collect data over a large timeframe;thus, we expect the number and size of moving regions representing storm clouds to be large. To implement our proposed query, we bring together the following concepts: (i) image processing to retrieve storm clouds from radar images, (ii) interpolation mechanisms to construct moving regions with infinite temporal resolution from region snapshots, (iii) transformations to compute exact point in moving polygon queries using 2-dimensional rather than 3-dimensional algorithms, (iv) GPU algorithms for massively parallel computation of the duration that a point lies inside a moving polygon, and (v) map/reduce algorithms to provide scalability. The resulting prototype lays the groundwork for building big data solutions for moving object databases.

关键词： graphics processing units image processing query processing radar imaging visual databases

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Frequency response and distortion properties of nonlinear image processing algorithms and the importance of imaging context

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MEDICAL PHYSICS 2013年第9期40卷 091906-091906页

作者： Wells, Jered R. Dobbins, James T., iii Duke Univ Med Ctr Carl E Ravin Adv Imaging Labs Durham NC 27705 USA Duke Univ Med Phys Grad Program Durham NC 27705 USA Duke Univ Dept Radiol Med Ctr Durham NC 27710 USA Duke Univ Dept Biomed Engn Durham NC 27708 USA Duke Univ Dept Phys Durham NC 27708 USA

Purpose: The most common metrics for resolution analysis in medical imaging are valid only for (approximately) linear systems. While analogues to these metrics have been used in attempts to describe resolution performance in nonlinear systems, the analysis is incomplete since distortion effects are often ignored. The authors have developed a methodology to analyze the amplitude modulation and waveform distortion properties of nonlinear systems with specific application to medical image processing algorithms. Methods: Using sinusoidal basis functions, two metrics were derived which distinguish amplitude modulation from nonlinear waveform distortion: principle frequency response and distortion power spectrum, respectively. Additionally, two figures of merit were developed to describe the relative impact of nonlinear distortion as a result of image processing: distortion index (DI) and Sigma DI. Three nonlinear denoising algorithms, the median, bilateral, and wavelet denoising filters, were selected as example functions to demonstrate the utility of the metrics derived in this study. Results: Each filter showed very different resolution and waveform distortion properties. In particular, the amplitude and extent of nonlinear distortion depended strongly on image context and the type of nonlinear mechanism employed. Nonlinear waveform distortion constituted up to 30% of the median filter output signal power in high contrast-to-noise ratio (CNR) scenarios. Conversely, nonlinear distortion never exceeded 1% of the bilateral filter output signal power. The wavelet denoising response contained between 1% and 9% distortion which varied weakly as a function of CNR. Conclusions: The analytical metrics described in the paper demonstrate the importance of considering both resolution-and distortion-related effects in characterizing the performance of nonlinear imaging systems with specific application to image processing algorithms. Findings with three common nonlinear algorithms

关键词： frequency response nonlinear resolution total harmonic distortion image processing algorithms

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Kernel Multivariate Analysis Framework for Supervised Subspace Learning

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IEEE SIGNAL processing MAGAZINE 2013年第4期30卷 16-29页

作者： Arenas-Garcia, Jeronimo Petersen, Kaare Brandt Camps-Valls, Gustavo Hansen, Lars Kai Tech Univ Denmark Cognit Syst Sect Lyngby Denmark

Feature extraction and dimensionality reduction are important tasks in many fields of science dealing with signal processing and analysis. The relevance of these techniques is increasing as current sensory devices are developed with ever higher resolution, and problems involving multimodal data sources become more common. A plethora of feature extraction methods are available in the literature collectively grouped under the field of Multivariate Analysis (MVA). This paper provides a uniform treatment of several methods: Principal Component Analysis (PCA), Partial Least Squares (PLS), Canonical Correlation Analysis (CCA) and Orthonormalized PLS (OPLS), as well as their non-linear extensions derived by means of the theory of reproducing kernel Hilbert spaces. We also review their connections to other methods for classification and statistical dependence estimation, and introduce some recent developments to deal with the extreme cases of large-scale and low-sized problems. To illustrate the wide applicability of these methods in both classification and regression problems, we analyze their performance in a benchmark of publicly available data sets, and pay special attention to specific real applications involving audio processing for music genre prediction and hyperspectral satellite images for Earth and climate monitoring.

关键词： Feature extraction Machine learning Learning systems Signal processing algorithms Principal component analysis Kernal Signal resolution Hilbert space Satellite broadcasting Satellite imaging Audio systems

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Segmentation of colonic polyps in NBI-forming material using gabor filter-based multi-channel level sets 1

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Workshops Bildverarbeitung fur die Medizin: Algorithmen - Systeme - Anwendungen, BVM 2013 - Workshop on Image processing for Medicine: algorithms - systems - Applications, BVM 2013

作者： Morariu, Cosmin Adrian Gross, Sebastian Pauli, Josef Aach, Til Lehrstuhl Intelligente Systeme Universität Duisburg-Essen Germany Lehrstuhl für Bildverarbeitung RWTH Aachen Germany Medizinische Klinik III Universitätsklinikum Aachen Germany

ISBN: (数字)9783642364808

ISBN: (纸本)9783642364792

Das Narrow Band imaging (NBI) erleichtert die Differenzierung zwischen Adenomen und harmlosen Darmwandgebilden (Hyperplasten) durch eine kontrastreiche Blutgefäßdarstellung. Dadurch wird eine automatische Segmentierung der Blutgefäße und Klassifizierung der Kolonpolypen hinsichtlich ihrer Entartungsfähigkeit ermöglicht. Eine erste Voraussetzung besteht in der automatisierten Trennung der Polypenoberfläche von der umliegenden Darmwand. In dieser Arbeit wird ein Verfahren vorgestellt, welches die Nachahmung der Fähigkeiten des Menschen zur Mustererkennung umsetzt. Die kombinierte Einbeziehung von Farbe und Textur gelingt mithilfe eines Gaußschen Farbmodells und Gaborfilterbasierten Aktiven Konturen. Eine Lokalisierungsgenauigkeit von 81 % und eine Spezifität von 94 % gewährleisten eine akkurate Polypensegmentierung durch die vorgestellte Level-Set-Methode und verprechen eine erfolgreiche Erkennung auch anderer Gewebe im Rahmen optischer sowie radiologischer Untersuchungen.

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DARK CHANNEL PRIOR-BASED SPATIALLY ADAPTIVE CONTRAST ENHANCEMENT FOR BACK LIGHTING COMPENSATION

DARK CHANNEL PRIOR-BASED SPATIALLY ADAPTIVE CONTRAST ENHANCE...

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IEEE International Conference on Acoustics, Speech, and Signal processing

作者： Jaehyun Im Inhye Yoon Monson H. Hayes Joonki Paik Image Processing and Intelligent Systems Laboratory Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University Seoul 156-756 Korea

ISBN: (纸本)9781479903573

In this paper, we present a novel contrast enhancement method for backlit images that consists of three steps: i) computation of the transmission coefficients using the dark channel prior, ii) generation of multiple images having different exposures based on the transmission coefficients, and iii) image fusion. Compared to global intensity transformation methods and spatially invariant contrast enhancement algorithms, our approach first extracts under-exposed regions using the dark channel prior map, and then performs spatially adaptive contrast enhancement. As a result, the contrast of the image is increased, especially for backlit scenes and those with very wide dynamic range, while still preserving image details and color.

关键词： contrast enhancement contrast enhancement method TRANSMISSION COEFFICIENT Lighting Backlights IMAGE CONTRAST multiple image compensated images wide dynamic range Imagery (Psychotherapy)

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A modular system architecture for online parallel vision pipelines

A modular system architecture for online parallel vision pip...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Jeremie Papon Alexey Abramov Eren Aksoy Florentin Wörgötter Bernstein Center for Computational Neuroscience (BCCN) III Physikalisches Institut-Biophysik Georg-August University of Göttingen Germany Bernstein Center for Computational Neuroscience (BCCN) III Physikalisches Institut-Biophysik Georg-August-University of Goettingen Germany

We present an architecture for real-time, online vision systems which enables development and use of complex vision pipelines integrating any number of algorithms. Individual algorithms are implemented using modular plugins, allowing integration of independently developed algorithms and rapid testing of new vision pipeline configurations. The architecture exploits the parallelization of graphics processing units (GPUs) and multi-core systems to speed processing and achieve real-time performance. Additionally, the use of a global memory management system for frame buffering permits complex algorithmic flow (e.g. feedback loops) in online processing setups, while maintaining the benefits of threaded asynchronous operation of separate algorithms. To demonstrate the system, a typical real-time system setup is described which incorporates plugins for video and depth acquisition, GPU-based segmentation and optical flow, semantic graph generation, and online visualization of output. Performance numbers are shown which demonstrate the insignificant overhead cost of the architecture as well as speed-up over strictly CPU and single threaded implementations.

关键词： Visualization Optical buffering Optical imaging Instruction sets Integrated optics

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Challenges of medical image processing

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COMPUTER SCIENCE-RESEARCH AND DEVELOPMENT 2011年第1-2期26卷 5-13页

作者： Scholl, Ingrid Aach, Til Deserno, Thomas M. Kuhlen, Torsten FH Aachen Univ Appl Sci Fac Elect Engn & Informat Technol Aachen Germany Rhein Westfal TH Aachen Inst Imaging & Comp Vis Aachen Germany Rhein Westfal TH Aachen Univ Hosp Aachen Dept Med Informat Aachen Germany Rhein Westfal TH Aachen Virtual Real Grp Aachen Germany

In todays health care, imaging plays an important role throughout the entire clinical process from diagnostics and treatment planning to surgical procedures and follow up studies. Since most imaging modalities have gone directly digital, with continually increasing resolution, medical image processing has to face the challenges arising from large data volumes. In this paper, we discuss Kilo-to Terabyte challenges regarding (i) medical image management and image data mining, (ii) bioimaging, (iii) virtual reality in medical visualizations and (iv) neuroimaging. Due to the increasing amount of data, image processing and visualization algorithms have to be adjusted. Scalable algorithms and advanced parallelization techniques using graphical processing units have been developed. They are summarized in this paper. While such techniques are coping with the Kilo-to Terabyte challenge, the Petabyte level is already looming on the horizon. For this reason, medical image processing remains a vital field of research.

关键词： Medical imaging Bioimaging Neuroimaging Visualization Giga-Voxel Tera-Voxel Picture archiving and communication systems (PACS) Content-based image retrieval (CBIR) Virtual reality (VR) Graphics processing unit (GPU) programming Parallel algorithm Grid computing

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