J. Imaging, Volume 4, Issue 5 (May 2018) – 13 articles

X-ray and neutron tomography are applied as a bi-modal approach for the 3D characterisation of a Monturaqui impactite formed by shock metamorphism during the impact of an iron meteorite with the target rocks in the Monturaqui crater (Chile). The particular impactite exhibits structural heterogeneities on many length scales: its composition is dominated by silicate-based glassy and crystalline materials with voids and Fe/Ni-metal and oxihydroxides particles generally smaller than 1 mm in diameter. The non-destructive investigation allowed us to apply a novel bi-modal imaging approach that provides a more detailed and quantitative understanding of the structural and chemical composition compared to standard single mode imaging methods, as X-ray and neutron interaction with matter results in different attenuation coefficients with a non-linear relation. The X-ray and neutron data sets have been registered, and used for material segmentation, porosity and metallic content characterization. The bimodal data enabled the segmentation of a large number of different materials, their morphology as well as distribution in the specimen including the quantification of volume fractions. The 3D data revealed an evaporite type of material in the impactite not noticed in previous studies. The present study is exemplary in demonstrating the potential for non-destructive characterisation of key features of complex multi-phase objects such as impactites. Full article

The paper provides a specific perspective view on background subtraction for moving object detection, as a building block for many computer vision applications, being the first relevant step for subsequent recognition, classification, and activity analysis tasks. Since color information is not sufficient for dealing with problems like light switches or local gradual changes of illumination, shadows cast by the foreground objects, and color camouflage, new information needs to be caught to deal with these issues. Depth synchronized information acquired by low-cost RGBD sensors is considered in this paper to give evidence about which issues can be solved, but also to highlight new challenges and design opportunities in several applications and research areas. Full article

Bridging the human mind with an external system implicitly or explicitly has been the aspiration of researchers working in the field of cognitive neuroimaging. Identifying the potential of various imaging techniques in identifying and mapping different regions of the brain in relation to their functions is the key to eliminating the difficulties in developing a mind-machine interface (MMI). Communication technology has flourished to the extent that wireless MMI applications can be designed to virtually control machines like wheelchairs, artificial limbs, etc. A cornucopia of diversified works on cognitive imaging is required to move the preliminary MMI models forward, thus engendering a technologically advanced system which can be operated directly by the brain. This article provides an overview of various aspects of cognitive neuroimaging and its potential applications in the development of a mind-machine interface. Full article

The recently developed speckle-based technique is a promising candidate for laboratory-based X-ray phase-contrast imaging due to its compatibility with polychromatic X-rays, multi-modality and flexibility. Previously, successful implementations of the method on laboratory systems have been shown mostly with energies less than 20 keV on samples with materials like soft tissues or polymer. Higher energy X-rays are needed for penetrating materials with a higher atomic number or that are thicker in size. A first demonstration using high energy X-rays was recently given. Here, we present more potential application examples, i.e., a multi-contrast imaging of an IC chip and a phase tomography of a mortar sample, at an average photon energy of 40 keV using a laboratory X-ray tube. We believe the results demonstrate the applicability of this technique in a wide range of fields for non-destructive examination in industry and material science. Full article

Bleed-through is a frequent, pervasive degradation in ancient manuscripts, which is caused by ink seeped from the opposite side of the sheet. Bleed-through, appearing as an extra interfering text, hinders document readability and makes it difficult to decipher the information contents. Digital image restoration techniques have been successfully employed to remove or significantly reduce this distortion. This paper proposes a two-step restoration method for documents affected by bleed-through, exploiting information from the recto and verso images. First, the bleed-through pixels are identified, based on a non-stationary, linear model of the two texts overlapped in the recto-verso pair. In the second step, a dictionary learning-based sparse image inpainting technique, with non-local patch grouping, is used to reconstruct the bleed-through-contaminated image information. An overcomplete sparse dictionary is learned from the bleed-through-free image patches, which is then used to estimate a befitting fill-in for the identified bleed-through pixels. The non-local patch similarity is employed in the sparse reconstruction of each patch, to enforce the local similarity. Thanks to the intrinsic image sparsity and non-local patch similarity, the natural texture of the background is well reproduced in the bleed-through areas, and even a possible overestimation of the bleed through pixels is effectively corrected, so that the original appearance of the document is preserved. We evaluate the performance of the proposed method on the images of a popular database of ancient documents, and the results validate the performance of the proposed method compared to the state of the art. Full article

The numerical calculation of the signal-to-noise ratio (SNR) of magnetic resonance imaging (MRI) coil arrays is a powerful tool in the development of new coil arrays. The proposed method describes a complete model that allows the calculation of the absolute SNR values of arbitrary coil arrays, including receiver chain components. A new method for the SNR calculation of radio frequency receive coil arrays for MRI is presented, making use of their magnetic $B_1^{-}$ transmit pattern and the S-parameters of the network. The S-parameters and $B_1^{-}$ fields are extracted from an electromagnetic field solver and are post-processed using our developed model to provide absolute SNR values. The model includes a theory for describing the noise of all components in the receiver chain and the noise figure of a pre-amplifier by a simple passive two-port network. To validate the model, two- and four-element receive coil arrays are investigated. The SNR of the examined arrays is calculated and compared to measurement results using imaging of a saline water phantom in a $3 \mathrm{T}$ scanner. The predicted values of the model are in good agreement with the measured values. The proposed method can be used to predict the absolute SNR for any receive coil array by calculating the transmit $B_1^{-}$ pattern and the S-parameters of the network. Knowledge of the components of the receiver chain including pre-amplifiers leads to satisfactory results compared to measured values, which proves the method to be a useful tool in the development process of MRI receive coil arrays. Full article

The electrocardiogram (ECG) is commonly utilized for patient monitoring during magnetic resonance imaging (MRI) despite known magnetohydrodynamic voltage (VMHD) overlays, which often eclipse the true sinus rhythm and render the signal to be non-diagnostic. This can complicate MRI gating and at-risk patient monitoring, causing alternative low-fidelity signals to become preferred. We aimed to develop a method of isolating the true sinus rhythm from VMHD in order to enable the use of high-fidelity ECGs during MRI procedures. Twelve-lead ECGs were acquired in two healthy volunteers (n = 2) in a 3T MRI scanner, while a secondary single lead monitor was positioned across the left common carotid artery to directly record VMHD while cancelling out the true sinus rhythm. Carotid MHD was used to adaptively train a least mean squares filter to update a 12-lead ECG VMHD template and produce: (1) clean 12-lead ECGs and (2) an accurate stroke volume (SV) estimate. The adaptive filtering method was shown to reduce VMHD in 12-lead ECGs. This was demonstrated by an average cross-correlation of 0.81 across all ECG leads calculated between filtered ECG taken inside the MRI scanner and the ECG taken outside the MRI scanner. Residual noise formed <5% of the R-wave amplitude. Additionally, the method required only a short training phase. A method to extract real sinus rhythm beats from intra-MRI 12-lead ECGs was presented and shown to provide accurate dynamic measurements of induced VMHD using flow in the carotid artery as a source of dynamic feedback. Full article

One of the most challenging computer vision problems in the plant sciences is the segmentation of roots and soil in X-ray tomography. So far, this has been addressed using classical image analysis methods. In this paper, we address this soil–root segmentation problem in X-ray tomography using a variant of supervised deep learning-based classification called transfer learning where the learning stage is based on simulated data. The robustness of this technique, tested for the first time with this plant science problem, is established using soil–roots with very low contrast in X-ray tomography. We also demonstrate the possibility of efficiently segmenting the root from the soil while learning using purely synthetic soil and roots. Full article

Pixelated images are used to transmit data between computing devices that have cameras and screens. Significant compression of pixelated images has been achieved by an “edge-based transformation and entropy coding” (ETEC) algorithm recently proposed by the authors of this paper. The study of ETEC is extended in this paper with a comprehensive performance evaluation. Furthermore, a novel algorithm termed “prediction-based transformation and entropy coding” (PTEC) is proposed in this paper for pixelated images. In the first stage of the PTEC method, the image is divided hierarchically to predict the current pixel using neighboring pixels. In the second stage, the prediction errors are used to form two matrices, where one matrix contains the absolute error value and the other contains the polarity of the prediction error. Finally, entropy coding is applied to the generated matrices. This paper also compares the novel ETEC and PTEC schemes with the existing lossless compression techniques: “joint photographic experts group lossless” (JPEG-LS), “set partitioning in hierarchical trees” (SPIHT) and “differential pulse code modulation” (DPCM). Our results show that, for pixelated images, the new ETEC and PTEC algorithms provide better compression than other schemes. Results also show that PTEC has a lower compression ratio but better computation time than ETEC. Furthermore, when both compression ratio and computation time are taken into consideration, PTEC is more suitable than ETEC for compressing pixelated as well as non-pixelated images. Full article

This paper provides a comprehensive analysis of the performance of hyperspectral imaging for detecting adulteration in red-meat products. A dataset of line-scanning images of lamb, beef, or pork muscles was collected taking into account the state of the meat (fresh, frozen, thawed, and packing and unpacking the sample with a transparent bag). For simulating the adulteration problem, meat muscles were defined as either a class of lamb or a class of beef or pork. We investigated handcrafted spectral and spatial features by using the support vector machines (SVM) model and self-extraction spectral and spatial features by using a deep convolution neural networks (CNN) model. Results showed that the CNN model achieves the best performance with a 94.4% overall classification accuracy independent of the state of the products. The CNN model provides a high and balanced F-score for all classes at all stages. The resulting CNN model is considered as being simple and fairly invariant to the condition of the meat. This paper shows that hyperspectral imaging systems can be used as powerful tools for rapid, reliable, and non-destructive detection of adulteration in red-meat products. Also, this study confirms that deep-learning approaches such as CNN networks provide robust features for classifying the hyperspectral data of meat products; this opens the door for more research in the area of practical applications (i.e., in meat processing). Full article

X-ray phase-contrast imaging is a promising method for medical imaging and non-destructive testing. Information about the attenuation, small-angle scattering and phase-shifting properties of an object can be gained simultaneously in three image modalities using a Talbot–Lau interferometer. This is a highly sensitive approach for retrieving this information. Nevertheless, until now, Talbot–Lau interferometry has been a time-consuming process due to image acquisition by phase-stepping procedures. Thus, methods to accelerate the image acquisition process in Talbot–Lau interferometry would be desirable. This is especially important for medical applications to avoid motion artifacts. In this work, the Talbot–Lau interferometry is combined with the moiré imaging approach. Firstly, the reconstruction algorithm of moiré imaging is improved compared to the standard reconstruction methods in moiré imaging that have been published until now. Thus, blurring artifacts resulting from the reconstruction in the frequency domain can be reduced. Secondly, the improved reconstruction algorithm allows for reducing artifacts in the reconstructed images resulting from inhomogeneities of the moiré pattern in large fields of view. Hence, the feasibility of differential phase-contrast imaging with regard to the integration into workflows in medical imaging and non-destructive testing is improved considerably. New fields of applications can be gained due to the accelerated imaging process—for example, live imaging in medical applications. Full article

The aim of this work is to construct an algorithm for visualizing a polydisperse phase of solid particles (dust) in an inhomogeneous flow of a two-phase gas-dust mixture that would allow us to see, within one plot, the degree of polydispersity of the dust phase and the difference in the spatial distributions of individual fractions of dust particles in the computational domain. The developed technique allows us to reproduce concentrations from one to three fractions of dust particles in each cell in the computational domain. Each of the three fractions of dust particles is mapped to one of the main channels of the RGB palette. The intensity of the color shade is set to be proportional to the relative concentration of dust particles in this fraction. The final image for a polydisperse mixture is obtained by adding images in each of the three color channels. To visualize the degree of polydispersity, we propose depicting the spatial distribution of the entropy of the dust mixture. The definition of the entropy of a mixture is generalized to take into account the states of a mixture with zero number of particles in the mixture. They correspond to dust-free sections of the computational domain (voids). The proposed method for visualizing the polydispersity of a mixture of particles is demonstrated using the example of dynamic numerical modeling of the spatial features of dust structures formed in turbulent gas-dust flows and in flows with shock waves. Full article

In the past few years, X-ray phase-contrast and dark-field imaging have evolved to be invaluable tools for non-destructive sample visualisation, delivering information inaccessible by conventional absorption imaging. X-ray phase-sensing techniques are furthermore increasingly used for at-wavelength metrology and optics characterisation. One of the latest additions to the group of differential phase-contrast methods is the X-ray speckle-based technique. It has drawn significant attention due to its simple and flexible experimental arrangement, cost-effectiveness and multimodal character, amongst others. Since its first demonstration at highly brilliant synchrotron sources, the method has seen rapid development, including the translation to polychromatic laboratory sources and extension to higher-energy X-rays. Recently, different advanced acquisition schemes have been proposed to tackle some of the main limitations of previous implementations. Current applications of the speckle-based method range from optics characterisation and wavefront measurement to biomedical imaging and materials science. This review provides an overview of the state of the art of the X-ray speckle-based technique. Its basic principles and different experimental implementations as well as the the latest advances and applications are illustrated. In the end, an outlook for anticipated future developments of this promising technique is given. Full article