Medical image segmentation has traditionally been regarded as a separate process fromimage acquisition and reconstruction, even though its performance directly depends on the quality and characteristics of these firs...
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ISBN:
(纸本)9783319104041;9783319104034
Medical image segmentation has traditionally been regarded as a separate process fromimage acquisition and reconstruction, even though its performance directly depends on the quality and characteristics of these first stages of the imaging pipeline. Adopting an integrated acquisition-reconstruction-segmentation process can provide a more efficient and accurate solution. In this paper we propose a joint segmentation and reconstruction algorithm for undersampled magnetic resonance data. Merging a reconstructive patch-based sparse modelling and a discriminative Gaussian mixture modelling can produce images with enhanced edge information ultimately improving their segmentation.
Although the topic of Super-Resolution reconstruction (SRR) has recently received considerable attention within the traditional research community, the SRR estimations are based on L1 or L2 statistical norm estimation...
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ISBN:
(纸本)9781424414369
Although the topic of Super-Resolution reconstruction (SRR) has recently received considerable attention within the traditional research community, the SRR estimations are based on L1 or L2 statistical norm estimation. Therefore, these SRR methods are very sensitive to their assumed data and noise models, which limit their applications. The real noise models that corrupt the measure sequence are unknown;consequently, SRR algorithm using L1 or L2 norm may degrade the image sequence rather than enhance it. The robust norm applicable to several noise and data models is desired in SRR algorithms. This paper proposes an alternate SRR approach based on the stochastic regularization technique of Bayesian MAP estimation by minimizing a cost function. The Lorentzian norm is used for measuring the difference between the projected estimate of the high-resolution image and each low resolution image, removing outliers in the data. Tikhonov regularization is used to remove artifacts from the final result and improve the rate of convergence. In order to cope with real sequences and complex motion sequences, the fast affine block-based registration is used in the registration step of SRR. The experimental results show that the proposed reconstruction can be applied on real sequences such as Suzie sequence and confirm the effectiveness of our method and demonstrate its superiority to other super-resolution methods based on L1 and L2 norm for several noise models such as AWGN, Poisson and Salt & Pepper noise.
We consider in this paper the problem of reconstructing 3D Computed Tomography images from limited data. The problem is modeled as a nonnegatively constrained minimization problem of very large size. In order to obtai...
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We consider in this paper the problem of reconstructing 3D Computed Tomography images from limited data. The problem is modeled as a nonnegatively constrained minimization problem of very large size. In order to obtain an acceptable image in short time, we propose a scaled gradient projection method, accelerated by exploiting a suitable scaling matrix and efficient rules for the choice of the step-length. In particular, we select the step-length either by alternating Barzilai-Borwein rules or by exploiting a limited number of back gradients for approximating second-order information. Numerical results on a 3D Shepp-Logan phantom are presented and discussed.
There remains an urgent need to develop effective photoacoustic computed tomography (PACT) imagereconstruction methods for use with acoustically inhomogeneous media. Transcranial PACT brain imaging is an important ex...
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ISBN:
(纸本)9780819493507
There remains an urgent need to develop effective photoacoustic computed tomography (PACT) imagereconstruction methods for use with acoustically inhomogeneous media. Transcranial PACT brain imaging is an important example of an emerging imaging application that would benefit greatly from this. Existing approaches to PACT imagereconstruction in acoustically heterogeneous media are limited to weakly varying media, are computationally burdensome, and/or make impractical assumptions regarding the measurement geometry. In this work, we develop and investigate a full-wave approach to iterative imagereconstruction in PACT for media possessing inhomogeneous speed-of-sound and mass density distributions. A key contribution of the work is the formulation of a procedure to implement a matched discrete forward and backprojection operator pair, which facilitates the application of a wide range of modern iterative imagereconstruction algorithms. This presents the opportunity to employ application-specific regularization methods to mitigate image artifacts due to mea- surement dataincompleteness and noise. Our results establish that the proposed imagereconstruction method can effectively compensate for acoustic aberration and reduces artifacts in the reconstructed image.
We present an algorithm for 3D object reconstruction on ultrasound images based on simplex meshes. The algorithm uses manually traced object boundaries on several representative non-parallel cross-sections of 3D US im...
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ISBN:
(纸本)9856107334
We present an algorithm for 3D object reconstruction on ultrasound images based on simplex meshes. The algorithm uses manually traced object boundaries on several representative non-parallel cross-sections of 3D US image. Simplex meshes are attracted to these contours under the influence of external forces, derived from the initial data. Several algorithms for external force generation are investigated.
We propose a global optimization framework for 3D shape reconstructionfrom sparse noisy 3D measurements frequently encountered in range scanning, sparse feature-based stereo, and shape-from-X. In contrast to earlier ...
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ISBN:
(纸本)9781424411795
We propose a global optimization framework for 3D shape reconstructionfrom sparse noisy 3D measurements frequently encountered in range scanning, sparse feature-based stereo, and shape-from-X. In contrast to earlier local or banded optimization methods for shape fitting, we compute global optimum in the whole volume removing dependence on initial guess and sensitivity to numerous local minima. Our global method is based on two main ideas. First, we suggest a new regularization functional with a data alignment term that maximizes the number of (weakly-oriented) data points contained by a surface while allowing for some measurement errors. Second, we propose a touch-expand algorithm for finding a minimum cut on a huge 3D grid using an automatically adjusted band This overcomes prohibitively high memory cost of graph cuts when computing globally optimal surfaces at high-resolution. Our results for sparse or incomplete 3D datafrom laser scanning and passive multi-view stereo are robust to noise, outliers, missing parts, and varying sampling density.
The traditional SRR (Super-Resolution reconstruction) estimations are based on L1 or L2 statistical norm estimation therefore these SRR methods are usually very sensitive to their assumed model of data and noise, whic...
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ISBN:
(纸本)1424404630
The traditional SRR (Super-Resolution reconstruction) estimations are based on L1 or L2 statistical norm estimation therefore these SRR methods are usually very sensitive to their assumed model of data and noise, which limits their utility. This paper reviews some of these methods and addresses their shortcomings. We propose an alternate SRR approach based on a statistical estimation technique. By minimizing a cost function, the Huber norm is used for measuring the difference between the projected estimate of the high-resolution image and each low resolution image, removing outliers in the data and Tikhonov regularization is used to remove artifacts from the final answer and improve the rate of convergence. The experimental results confirm the effectiveness of our method and demonstrate its superiority to other super-resolution methods based on L1 and L2 norm for a several noise models such as noiseless, AWGN, Poisson and Salt&Pepper Noise.
Single-photon emission computed tomography (SPECT) is a method of choice for imaging spatial distributions of radioisotopes. Applications of this method are found in medicine, biomedical research and nuclear industry....
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ISBN:
(纸本)9781457709258
Single-photon emission computed tomography (SPECT) is a method of choice for imaging spatial distributions of radioisotopes. Applications of this method are found in medicine, biomedical research and nuclear industry. This paper deals with improving spatial resolution in SPECT by applying correction for the point-spread function (PSF) in the reconstruction algorithm and optimizing the collimator. Several approaches are considered: the use of a depth-dependent PSF model for a parallel-beam collimator derived from experimental data, the extension of this model to a fan-beam collimator, a triangular approximation of the PSF for reconstruction acceleration, and a method for optimal fan-beam collimator design. An unmatched projector/backprojector ordered subsets expectation maximization (OSEM) algorithm is used for imagereconstruction. Experimental results with simulated and physical phantom data of a micro-SPECT system show a significant improvement of spatial resolution with the proposed methods.
Iterative reconstruction is a good match with the sparsely sampled limited angle data generated by breast tomosynthesis systems. However, it suffers from a specific artifact near the breast edge where it overestimates...
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ISBN:
(纸本)9781510625440
Iterative reconstruction is a good match with the sparsely sampled limited angle data generated by breast tomosynthesis systems. However, it suffers from a specific artifact near the breast edge where it overestimates the x-ray path length, resulting in a considerable underestimation of the reconstructed linear attenuation coefficients. In this work, we present the application of a method that uses the measured 3D breast shape to reduce these artifacts in patient data, by including this information as an additional constraint in the imagereconstruction process. A series of 50 patients undergoing breast tomosynthesis were additionally imaged with a pair of structured light cameras placed left and right of the mammography unit. These 3D surfaces were then aligned with the help of the backprojected breast outline from the x-ray data to form a single contour following the true breast shape. This was then further processed to generate a binary 3D mask set to 1 inside and to 0 outside the breast, and used as constraint in the reconstruction. Due to incomplete coverage and image artifacts, this mask was created successfully for only 19 out of 50 cases. reconstructions were created with and without this constraint, and comparing attenuation profiles found that the artifact was almost completely corrected, bringing the reconstructed attenuation near the breast edge to the same level as the central region. Further visual inspection does show that higher quality optical 3D measurements and more precise alignment between optical and x-ray data are needed to avoid introducing new artifacts in the reconstruction.
This paper presents the exact maximum likelihood (ML) estimate of temperature for a class of Markov random fields (MRF) known as generalized Gaussian MRFs. The ML estimate has a simple closed form which is analogous t...
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