A semi-automatic algorithm for grey level estimation in tomography

Batenburg, Kees Joost; Aarle, Wim van; Sijbers, Jan

doi:10.1016/j.patrec.2010.09.001

Cited by 21 publications

(15 citation statements)

References 11 publications

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“…Manual adjustments were applied to the segmentation at the boundaries, where the segmentation was distorted by noise. The gray values for this dataset were estimated using the algorithm proposed by Batenburg et al [19]. The segmentation can be used as a ground truth.…”

Section: Experiments IVmentioning

confidence: 99%

SDART: An algorithm for discrete tomography from noisy projections

Bleichrodt

Tabak

Batenburg

2014

Computer Vision and Image Understanding

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Computed tomography is a noninvasive technique for reconstructing an object from projection data. If the object consists of only a few materials, discrete tomography allows us to use prior knowledge of the gray values corresponding to these materials to improve the accuracy of the reconstruction. The Discrete Algebraic Reconstruction Technique (DART) is a reconstruction algorithm for discrete tomography. DART can result in accurate reconstructions, computed by iteratively refining the boundary of the object. However, this boundary update is not robust against noise and DART does not work well when confronted with high noise levels.In this paper we propose a modified DART algorithm, which imposes a set of soft constraints on the pixel values. The soft constraints allow noise to be spread across the whole image domain, proportional to these constraints, rather than across boundaries. The results of our numerical experiments show that SDART yields more accurate reconstructions, compared to DART, if the signal-to-noise ratio is low.

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Section: Experiments IVmentioning

confidence: 99%

SDART: An algorithm for discrete tomography from noisy projections

Bleichrodt

Tabak

Batenburg

2014

Computer Vision and Image Understanding

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“…However, they can also be estimated in a semi-automatic way as described in Ref. 24. After the reconstruction of the separate bands, the HR image is composed.…”

Section: Color License Platesmentioning

confidence: 99%

Discrete algebraic reconstruction technique: a new approach for superresolution reconstruction of license plates

Zefreh¹,

Aarle²,

Batenburg³

et al. 2013

J. Electron. Imaging

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Abstract.A new superresolution algorithm is proposed to reconstruct a high-resolution license plate image from a set of low-resolution camera images. The reconstruction methodology is based on the discrete algebraic reconstruction technique (DART), a recently developed reconstruction method. While DART has already been successfully applied in tomographic imaging, it has not yet been transferred to the field of camera imaging. DART is introduced for camera imaging through a demonstration of how prior knowledge of the colors of the license plate can be directly exploited during the reconstruction of a high-resolution image from a set of low-resolution images. Simulation experiments show that DART can reconstruct images with superior quality compared to conventional reconstruction methods.

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“…Batenburg et al [9] proposed a semi-automatic algorithm for intensity level estimation, which requires the user to select manually regions that are expected to belong to the same gray level. Lukić [10] combined the multi-well potential function into the object function to encourage the solution staying on gray level values, but it's not an easy task to design the potential function without trapping the the solution in the local minimum.…”

Section: Introductionmentioning

confidence: 99%

Level estimation for sparse reconstruction in discrete tomography

Lin

Ortega

Dimakis

2011

2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR)

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In discrete tomography (DT), the goal is to reconstruct from multiple linear projections an unknown image, which is known to have few distinct pixel level intensities. Such images arise in tomography problems where very high contrast is expected, e.g., in angiography medical imaging. A common assumption for DT is that the set of possible intensity levels is known in advance. However, determining the intensity levels is a difficult problem, coupled with measurement calibration and the used reconstruction algorithm. We introduce an unsupervised DT algorithm that jointly reconstructs the image and estimates the unknown intensity levels. Our algorithm alternates between (i) an l 1 sparse recovery step with a reweighed cost function that pushes the reconstructed values close to the estimated intensities, and (ii) an estimation step for the most likely intensity levels. We experimentally demonstrate that the proposed algorithm successfully estimates the unknown levels and leads to high quality reconstruction of angiographic images.

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A semi-automatic algorithm for grey level estimation in tomography

Cited by 21 publications

References 11 publications

SDART: An algorithm for discrete tomography from noisy projections

SDART: An algorithm for discrete tomography from noisy projections

Discrete algebraic reconstruction technique: a new approach for superresolution reconstruction of license plates

Level estimation for sparse reconstruction in discrete tomography

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