Formulation of image fusion as a constrained least squares optimization problem

Dwork, Nicholas; Lasry, Eric M.; Pauly, John M.; Balbás, Jorge

doi:10.1117/1.jmi.4.1.014003

Cited by 4 publications

(4 citation statements)

References 33 publications

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“…Note that one is able to better comprehend where in the prostate the metabolic activity is taking place with the interpolated imagery. Figure 8 shows the imagery of figure 7 converted to a false color image and then fused with the original T2 weighted proton imagery using the CLS fusion algorithm [27]. (This fusion algorithm retains color while incorporating spatial information during fusion; this is especially important for false color imagery, where the color corresponds to a quantitative value.)…”

Section: Prostatementioning

confidence: 99%

Di-chromatic Interpolation of Magnetic Resonance Metabolic Imagery

Dwork,

Gordon,

Tang

et al. 2020

Preprint

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Magnetic resonance imaging with hyperpolarized contrast agents can provide unprecedented in vivo measurements of metabolism, but yields images that are lower resolution than that achieved when imaging water. To simultaneously display both images to the user, perhaps by fusing the images together, one must first interpolate the metabolic image to be the same size as that of the proton image. Common choices for doing so include nearest-neighbor interpolation and linear interpolation. In this work, we present a method that uses the proton imagery to inform the interpolation values of the metabolic image. The interpolated image is the result of a convex optimization algorithm which is solved with the Fast Iterative Shrinkage Threshold Algorithm (FISTA). Results are shown with imagery of hyperpolarized pyruvate from data of the prostate and heart.

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Section: Prostatementioning

confidence: 99%

Di-chromatic Interpolation of Magnetic Resonance Metabolic Imagery

Dwork,

Gordon,

Tang

et al. 2020

Preprint

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“…A fast image fusion approach is developed based on the weighted average of the images to be fused. For example, Dwork et al 21 presented a constrained least squares image fusion algorithm for color image fusion. We here introduce an adaptive image fusion method for MV image correction using ϕ Ã ðxÞ as an adaptive weight function, where the corrected MV image is given by E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 0 8 ; 6 3 ; 3 1 8Î 2 ðxÞ ¼ ½1 − ϕ Ã ðxÞI 2 ðxÞ þ ϕ Ã ðxÞI 1 ðxÞ:…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…Compared with other fusion methods, [21][22][23][24] our fusion method uses an adaptive weight function.…”

Section: Proposed Algorithmmentioning

confidence: 99%

Signal dropout correction-based ultrasound segmentation for diastolic mitral valve modeling

et al. 2018

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Three-dimensional ultrasound segmentation of mitral valve (MV) at diastole is helpful for duplicating geometry and pathology in a patient-specific dynamic phantom. The major challenge is the signal dropout at leaflet regions in transesophageal echocardiography image data. Conventional segmentation approaches suffer from missing sonographic data leading to inaccurate MV modeling at leaflet regions. This paper proposes a signal dropout correction-based ultrasound segmentation method for diastolic MV modeling. The proposed method combines signal dropout correction, image fusion, continuous max-flow segmentation, and active contour segmentation techniques. The signal dropout correction approach is developed to recover the missing segmentation information. Once the signal dropout regions of TEE image data are recovered, the MV model can be accurately duplicated. Compared with other methods in current literature, the proposed algorithm exhibits lower computational cost. The experimental results show that the proposed algorithm gives competitive results for diastolic MV modeling compared with conventional segmentation algorithms, evaluated in terms of accuracy and efficiency.

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“…In contrast with some existing fusion approaches, which merge multiple images in a spatial or wavelet domain [27]- [29], our method reconstructs a single image by fusing multiple measurement types at different spatial scales while exploiting their respective propagation models. In spirit, our approach to fusion relates to that of [30], where a convex optimization problem is devised to pansharpen medical images.…”

mentioning

confidence: 99%

Multi-Modal Non-Line-of-Sight Passive Imaging

Beckus

Tamasan

Atia

2019

IEEE Trans. on Image Process.

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We consider the non-line-of-sight (NLOS) imaging of an object using the light reflected off a diffusive wall. The wall scatters incident light such that a lens is no longer useful to form an image. Instead, we exploit the 4D spatial coherence function to reconstruct a 2D projection of the obscured object. The approach is completely passive in the sense that no control over the light illuminating the object is assumed and is compatible with the partially coherent fields ubiquitous in both the indoor and outdoor environments. We formulate a multi-criteria convex optimization problem for reconstruction, which fuses the reflected field's intensity and spatial coherence information at different scales. Our formulation leverages established optics models of light propagation and scattering and exploits the sparsity common to many images in different bases. We also develop an algorithm based on the alternating direction method of multipliers to efficiently solve the convex program proposed. A means for analyzing the null space of the measurement matrices is provided as well as a means for weighting the contribution of individual measurements to the reconstruction. This paper holds promise to advance passive imaging in the challenging NLOS regimes in which the intensity does not necessarily retain distinguishable features and provides a framework for multimodal information fusion for efficient scene reconstruction.

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Formulation of image fusion as a constrained least squares optimization problem

Cited by 4 publications

References 33 publications

Di-chromatic Interpolation of Magnetic Resonance Metabolic Imagery

Di-chromatic Interpolation of Magnetic Resonance Metabolic Imagery

Signal dropout correction-based ultrasound segmentation for diastolic mitral valve modeling

Multi-Modal Non-Line-of-Sight Passive Imaging

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