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Mobile application for two-stage linear and nonlinear image processing 6

Mobile application for two-stage linear and nonlinear image ...

引用

6th International Conference on Information Technology and Nanotechnology (IEEE ITNT)

作者： Medvedeva, Kseniya Fursov, Vladimir Samara Natl Res Univ Samara Russia Samara Natl Res Univ Image Proc Syst Inst RAS Branch FSRC Crystallog & Photon Samara Russia

ISBN: (纸本)9781728170411

The article discusses the technology of correcting blurred type distortions on images recorded by mobile devices. The aim of the development is to provide high-quality distortion correction with minimal computational costs. We use two-stage technology. At the first stage, blind identification of linear filter parameters is performed. It is assumed that the frequency response of the filter has central symmetry and consists of segments of quadratic and exponential functions. At the second stage, parameters of nonlinear filter are determined. Both automatic and manual visual settings are provided. The program code for Android OS and the results of experiments showing the effectiveness of the developed application to eliminate distortions in images are presented.

关键词： image processing mobile application linear image processing non-linear image processing

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Mathematical morphology approaches for fingerprints thinning

Mathematical morphology approaches for fingerprints thinning

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36th Annual International Carnahan Conference on Security Technology

作者： Espinosa-Duró, V Univ Politecn Catalunya EUPMt Elect & Automat Dept Mataro 08303 BCN Spain

ISBN: (纸本)0780374363

Our paper describes the design and implementation of a fingerprint thinning algorithm by means of morphological image processing operations in order to improve the minutiae map involved. The final goal will aim to incorporate the thinning algorithm proposed with the minutiae extraction algorithm for fingerprint identification systems, developed and presented at IEEE ICCST'01 [1].

关键词： mathematical morphology non-linear image processing fingerprint recognition

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4D Iterative Reconstruction in Cardiac CT

4D Iterative Reconstruction in Cardiac CT

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Conference on Medical Imaging - Physics of Medical Imaging

作者： Bruder, H. Raupach, R. Allmendinger, T. Sunnegardh, J. Stierstorfer, K. Flohr, T. Comp Tomog Siemens Healthcare Forchheim Germany

ISBN: (纸本)9780819489623

In this paper, a 4D iterative reconstruction scheme is developed to improve noise characteristics and/or reduce radiation exposure in multi-slice cardiac CT. In our implementation, image volume datasets are reconstructed at adjacent temporal positions with respect to an optimized cardiac phase (best phase). nonlinear regularization priors operate on a 4D cube surrounding each voxel in 4D space, reducing image noise while maintaining temporal and spatial image sharpness. The temporal resolution of image data is maintained despite the usage of temporal data that can substantially exceed the reconstruction range of the 'best phase' reconstruction. Consequently, the noise statistics is significantly improved because non-correlated image data at different temporal positions are utilized. To reduce the high computational load, the iterative regularization in 4D can be transferred into image space. Raw data based Iterative Reconstruction reducing artifacts due to the non-exactness of the backprojector is decoupled from regularization and restricted to only those projection data belonging to the 'best phase' reconstruction. Finally, the image formation is achieved by a normalized combination of the low frequency part of the raw data based Iterative Reconstruction at cardiac best phase and the high frequency part of the image based regularization image at best phase. We demonstrate the potential of noise reduction on basis of clinical cardiac CT data. As an example for cardiac dual source CT (DSCT) data a noise reduction up to 70% was achieved. Even in case of a very high and irregular heart beat with an average heart rate of 115 bpm the high temporal resolution of DSCT could be maintained.

关键词： Iterative Reconstruction non-linear image processing Cardiac Computed Tomography Multi-slice Computed Tomography Dual Source Computed Tomography

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Novel Iterative Reconstruction method for optimal dose usage in redundant CT-acquisitions

Novel Iterative Reconstruction method for optimal dose usage...

引用

Medical Imaging - Physics of Medical Imaging

作者： Bruder, H. Raupach, R. Allmendinger, T. Kappler, S. Sunnegardh, J. Stierstorfer, K. Flohr, T. Siemens Sect Healthcare Computed Tomog Forchheim Germany

ISBN: (纸本)9780819498267

In CT imaging, a variety of applications exist where reconstructions are SNR and/or resolution limited. However, if the measured data provide redundant information, composite image data with high SNR can be computed. Generally, these composite image volumes will compromise spectral information and/or spatial resolution and/or temporal resolution. This brings us to the idea of transferring the high SNR of the composite image data to low SNR (but high resolution) 'source' image data. It was shown that the SNR of CT image data can be improved using iterative reconstruction [1]. We present a novel iterative reconstruction method enabling optimal dose usage of redundant CT measurements of the same body region. The generalized update equation is formulated in image space without further referring to raw data after initial reconstruction of source and composite image data. The update equation consists of a linear combination of the previous update, a correction term constrained by the source data, and a regularization prior initialized by the composite data. The efficiency of the method is demonstrated for different applications: (i) Spectral imaging we have analysed material decomposition data from dual energy data of our photon counting prototype scanner: the material images can be significantly improved transferring the good noise statistics of the 20 keV threshold image data to each of the material images. (ii) Multi-phase liver imaging: Reconstructions of multi-phase liver data can be optimized by utilizing the noise statistics of combined data from all measured phases (iii) Helical reconstruction with optimized temporal resolution: splitting up reconstruction of redundant helical acquisition data into a short scan reconstruction with Tam window optimizes the temporal resolution The reconstruction of full helical data is then used to optimize the SNR. (iv) Cardiac imaging: the optimal phase image (best phase') can be improved by transferring all applied over radiation

关键词： Iterative Reconstruction Gaussian noise model Low-Contrast Detectability spatial resolution non-linear image processing Dual Energy imaging photon counting detector Multi-phase liver imaging Cardiac imaging Dual Source Computed Tomography

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Adaptive Iterative Reconstruction

Adaptive Iterative Reconstruction

引用

Conference on Medical Imaging 2011 - Physics of Medical Imaging

作者： Bruder, H. Raupach, R. Sunnegardh, J. Sedlmair, M. Stierstorfer, K. Flohr, T. Siemens Sect Healthcare Computed Tomog Forchheim Germany

ISBN: (纸本)9780819485038

It is well known that, in CT reconstruction, Maximum A Posteriori (MAP) reconstruction based on a Poisson noise model can be well approximated by Penalized Weighted Least Square (PWLS) minimization based on a data dependent Gaussian noise model. We study minimization of the PWLS objective function using the Gradient Descent (GD) method, and show that if an exact inverse of the forward projector exists, the PWLS GD update equation can be translated into an update equation which entirely operates in the image domain In case of non-linear regularization and arbitrary noise model this means that a non-linear image filter must exist which solves the optimization problem. In the general case of non-linear regularization and arbitrary noise model, the analytical computation is not trivial and might lead to image filters which are computationally very expensive. We introduce a new iteration scheme in image space, based on a regularization filter with an anisotropic noise model. Basically, this approximates the statistical data weighting and regularization in PWLS reconstruction. If needed, e.g. for compensation of the non-exactness of backprojector, the image-based regularization loop can be preceded by a raw data based loop without regularization and statistical data weighting. We call this combined iterative reconstruction scheme Adaptive Iterative Reconstruction (AIR). It will be shown that in terms of low-contrast visibility, sharpness-to-noise and contrast-to-noise ratio, PWLS and AIR reconstruction are similar to a high degree of accuracy. In clinical images the noise texture of AIR is also superior to the more artificial texture of PWLS.

关键词： Iterative Reconstruction Statistical Iterative Reconstruction Algebraic Reconstruction Technique Gaussian noise model low-contrast visibility sharpness-to-noise non-linear image processing Multi-slice Computed Tomography Dual Source Computed Tomography

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