Phase quantification using deep neural network processing of XRD patterns.

Mineral identification and quantification are key to the understanding and, hence, the capacity to predict material properties. The method of choice for mineral quantification is powder X-ray diffraction (XRD), generally using a Rietveld refinement approach. However, a successful Rietveld refinement requires preliminary identification of the phases that make up the sample. This is generally carried out manually, and this task becomes extremely long or virtually impossible in the case of very large datasets such as those from synchrotron X-ray diffraction computed tomography. To circumvent this issue, this article proposes a novel neural network (NN) method for automating phase identification and quantification. An XRD pattern calculation code was used to generate large datasets of synthetic data that are used to train the NN. This approach offers significant advantages, including the ability to construct databases with a substantial number of XRD patterns and the introduction of extensive variability into these patterns. To enhance the performance of the NN, a specifically designed loss function for proportion inference was employed during the training process, offering improved efficiency and stability compared with traditional functions. The NN, trained exclusively with synthetic data, proved its ability to identify and quantify mineral phases on synthetic and real XRD patterns. Trained NN errors were equal to 0.5% for phase quantification on the synthetic test set, and 6% on the experimental data, in a system containing four phases of contrasting crystal structures (calcite, gibbsite, dolomite and hematite). The proposed method is freely available on GitHub and allows for major advances since it can be applied to any dataset, regardless of the mineral phases present.

Cam-Unet: Print-Cam Image Correction for Zero-Bit Fourier Image Watermarking.

Image watermarking often involves the use of handheld devices under non-structured conditions for authentication purposes, particularly in the print-cam process where smartphone cameras are used to capture watermarked printed images. However, these images frequently suffer from perspective distortions, making them unsuitable for automated information detection. To address this issue, Cam-Unet, an end-to-end neural network architecture, is presented to predict the mapping from distorted images to rectified ones, specifically tailored for print-cam challenges applied to ID images. Given the limited availability of large-scale real datasets containing ground truth distortions, we created an extensive synthetic dataset by subjecting undistorted images to print-cam attacks. The proposed network is trained on this dataset, using various data augmentation techniques to improve its generalization capabilities. Accordingly, this paper presents an image watermarking system for the print-cam process. The approach combines Fourier transform-based watermarking with Cam-Unet as perspective distortion correction. Results show that the proposed method outperforms existing watermarking approaches typically employed to counter print-cam attacks and achieves an optimal balance between efficiency and cost-effectiveness.

Segmentation of Plantar Foot Thermal Images Using Prior Information.

Diabetic foot (DF) complications are associated with temperature variations. The occurrence of DF ulceration could be reduced by using a contactless thermal camera. The aim of our study is to provide a decision support tool for the prevention of DF ulcers. Thus, the segmentation of the plantar foot in thermal images is a challenging step for a non-constraining acquisition protocol. This paper presents a new segmentation method for plantar foot thermal images. This method is designed to include five pieces of prior information regarding the aforementioned images. First, a new energy term is added to the snake of Kass et al. in order to force its curvature to match that of the prior shape, which has a known form. Second, we defined the initial contour as the downsized prior-shape contour, which is placed inside the plantar foot surface in a vertical orientation. This choice makes the snake avoid strong false boundaries present outside the plantar region when evolving. As a result, the snake produces a smooth contour that rapidly converges to the true boundaries of the foot. The proposed method is compared to two classical prior-shape snake methods, that of Ahmed et al. and that of Chen et al. A database of 50 plantar foot thermal images was processed. The results show that the proposed method outperforms the previous two methods with a root-mean-square error of 5.12 pixels and a dice similarity coefficient of 94%. The segmentation of the plantar foot regions in the thermal images helped us to assess the point-to-point temperature differences between the two feet in order to detect hyperthermia regions. The presence of such regions is the pre-sign of ulcers in the diabetic foot. Furthermore, our method was applied to hyperthermia detection to illustrate the promising potential of thermography in the case of the diabetic foot. Associated with a friendly acquisition protocol, the proposed segmentation method is the first step for a future mobile smartphone-based plantar foot thermal analysis for diabetic foot patients.

Diabetic foot thermal image segmentation using Double Encoder-ResUnet (DE-ResUnet).

The use of thermography in the early diagnosis of Diabetic Foot (DF) has proven its effectiveness in identifying areas of the plantar foot that are susceptible to ulcer development. Segmentation of the foot sole is one of the most pertinent technical issues that must be performed with great precision. However, because of the inherent difficulties of foot thermal images, such as unclarity and the existence of ambiguities, segmentation approaches have not demonstrated sufficiently accurate and reliable results for clinical use. In this study, we aim to develop a fully automated, robust and accurate segmentation of the diabetic foot. To this end, we propose a deep neural network architecture adopting the encoder-decoder concept called Double Encoder-ResUnet (DE-ResUnet). This network combines the strengths of residual network and U-Net architecture. Moreover, it takes advantage of RGB (Red, Green, Blue) colour images and fuses thermal and colour information to improve segmentation accuracy. Our database consists of 398 pairs of thermal and RGB images. The population includes two groups. The first group of 54 healthy subjects. And a second group of 145 diabetic patients from the National Hospital Dos de Mayo in Peru. The dataset is splitted into 50% for training, 25% for validation and the last 25% is used for testing. This proposed model provided robust and accurate automatic segmentations of the DF and outperformed other state of the art methods with an average intersection over union (IoU) of 97%. In addition, it is able to accurately delineate the part of toes and heels which are high risk regions for ulceration.

Assessment of the non-Gaussianity and non-linearity levels of simulated sEMG signals on stationary segments.

The purpose of this paper was to evaluate the effects of the longitudinal single differential (LSD), the longitudinal double differential (LDD) and the normal double differential (NDD) spatial filters, the electrode shape, the inter-electrode distance (IED) on non-Gaussianity and non-linearity levels of simulated surface EMG (sEMG) signals when the maximum voluntary contraction (MVC) varied from 10% to 100% by a step of 10%. The effects of recruitment range thresholds (RR), the firing rate (FR) strategy and the peak firing rate (PFR) of motor units were also considered. A cylindrical multilayer model of the volume conductor and a model of motor unit (MU) recruitment and firing rate were used to simulate sEMG signals in a pool of 120 MUs for 5s. Firstly, the stationarity of sEMG signals was tested by the runs, the reverse arrangements (RA) and the modified reverse arrangements (MRA) tests. Then the non-Gaussianity was characterised with bicoherence and kurtosis, and non-linearity levels was evaluated with linearity test. The kurtosis analysis showed that the sEMG signals detected by the LSD filter were the most Gaussian and those detected by the NDD filter were the least Gaussian. In addition, the sEMG signals detected by the LSD filter were the most linear. For a given filter, the sEMG signals detected by using rectangular electrodes were more Gaussian and more linear than that detected with circular electrodes. Moreover, the sEMG signals are less non-Gaussian and more linear with reverse onion-skin firing rate strategy than those with onion-skin strategy. The levels of sEMG signal Gaussianity and linearity increased with the increase of the IED, RR and PFR.

Analytical performance bounds for multi-tensor diffusion-MRI.

PURPOSE: To examine the effects of MR acquisition parameters on brain white matter fiber orientation estimation and parameter of clinical interest in crossing fiber areas based on the Multi-Tensor Model (MTM). MATERIAL AND METHODS: We compute the Cramér-Rao Bound (CRB) for the MTM and the parameter of clinical interest such as the Fractional Anisotropy (FA) and the dominant fiber orientations, assuming that the diffusion MRI data are recorded by a multi-coil, multi-shell acquisition system. Considering the sum-of-squares method for the reconstructed magnitude image, we introduce an approximate closed-form formula for Fisher Information Matrix that has the simplicity and easy interpretation advantages. In addition, we propose to generalize the FA and the mean diffusivity to the multi-tensor model. RESULTS: We show the application of the CRB to reduce the scan time while preserving a good estimation precision. We provide results showing how the increase of the number of acquisition coils compensates the decrease of the number of diffusion gradient directions. We analyze the impact of the b-value and the Signal-to-Noise Ratio (SNR). The analysis shows that the estimation error variance decreases with a quadratic rate with the SNR, and that the optimum b-values are not unique but depend on the target parameter, the context, and eventually the target cost function. CONCLUSION: In this study we highlight the importance of choosing the appropriate acquisition parameters especially when dealing with crossing fiber areas. We also provide a methodology for the optimal tuning of these parameters using the CRB.

Use of an image restoration process to improve spatial resolution in bioluminescence imaging.

To improve spatial resolution in in vivo bioluminescence imaging, a photon scattering correction, image restoration method was tested. The chosen algorithm was tested on in vivo bioluminescent images acquired on three representative tumor models: subcutaneous, pulmonary, and disseminated peritoneal. Tumor size was chosen as a quantitative criterion, such that the tumor reference measurements (determined photographically or by computed tomography) were compared to those derived from bioluminescent images, before and after restoration. This technique allowed a significant reduction to be achieved in the relative error between reference measurements and dimensions derived from bioluminescent images. In addition, improved delineation of the tumor foci was achieved. The restoration method allows spatial resolution in bioluminescence imaging to be improved, with interesting perspectives in terms of staging and quantitation in experimental oncology.

Estimation of the 3D self-similarity parameter of trabecular bone from its 2D projection.

It has been shown that the analysis of two dimensional (2D) bone X-ray images based on the fractional Brownian motion (fBm) model is a good indicator for quantifying alterations in the three dimensional (3D) bone micro-architecture. However, this 2D measurement is not a direct assessment of the 3D bone properties. In this paper, we first show that S(3D), the self-similarity parameter of 3D fBm, is linked to S(2D), that of its 2D projection, by S(3D)=S(2D)-0.5. In the light of this theoretical result, we have experimentally examined whether this relation holds for trabecular bone. Twenty one specimens of trabecular bone were derived from frozen human femoral heads. They were digitized using a high resolution mu-CT. Their projections were simulated numerically by summing the data in the three orthogonal directions and both 3D and 2D self-similarity parameters were measured. Results show that the self-similarity of the 3D bone volumes and that of their projections are linked by the previous equation. This demonstrates that a simple projection provides 3D information about the bone structure. This information can be a valuable adjunct to the bone mineral density for the early diagnosis of osteoporosis.

A new anisotropy index on trabecular bone radiographic images using the fast Fourier transform.

BACKGROUND: The degree of anisotropy (DA) on radiographs is related to bone structure, we present a new index to assess DA. METHODS: In a region of interest from calcaneus radiographs, we applied a Fast Fourier Transform (FFT). All the FFT spectra involve the horizontal and vertical components corresponding respectively to longitudinal and transversal trabeculae. By visual inspection, we measured the spreading angles: Dispersion Longitudinal Index (DLI) and Dispersion Transverse Index (DTI) and calculated DA = 180/(DLI+DTI). To test the reliability of DA assessment, we synthesized images simulating radiological projections of periodic structures with elements more or less disoriented. RESULTS: Firstly, we tested synthetic images which comprised a large variety of structures from highly anisotropic structure to the almost isotropic, DA was ranging from 1.3 to 3.8 respectively. The analysis of the FFT spectra was performed by two observers, the Coefficients of Variation were 1.5% and 3.1 % for intra-and inter-observer reproducibility, respectively. In 22 post-menopausal women with osteoporotic fracture cases and 44 age-matched controls, DA values were respectively 1.87 +/- 0.15 versus 1.72 +/- 0.18 (p = 0.001). From the ROC analysis, the Area Under Curve (AUC) were respectively 0.65, 0.62, 0.64, 0.77 for lumbar spine, femoral neck, total femoral BMD and DA. CONCLUSION: The highest DA values in fracture cases suggest that the structure is more anisotropic in osteoporosis due to preferential deletion of trabeculae in some directions.

Anisotropy changes in post-menopausal osteoporosis: characterization by a new index applied to trabecular bone radiographic images.

Bone intrinsic strength is conditioned by several factors, including material property and trabecular micro-architecture. Bone mineral density (BMD) is a good surrogate for material property. Architectural anisotropy is of special interest in mechanics-architecture relations and characterizes the degree of directional organization of a material. We have developed anisotropy indices from the Fast Fourier Transform (FFT) on bone radiographs. We have validated these indices in a cross-sectional uni-center case-control study including 39 postmenopausal women with vertebral fracture and 70 age-matched control cases. BMD was measured at the lumbar spine and femoral neck. A fractal analysis of texture was performed on calcaneus radiographs at three regions of interest (ROIs), and the result was expressed as the H parameter (fractal dimension =H-2). The anisotropy evaluation was based on the FFT spectrum of these three ROIs extracted on calcaneus radiographs. On the FFT spectrum, we have measured the spreading angle of the longitudinal trabeculae called the dispersion longitudinal index (DLI) and the spreading angle of the transversal trabeculae called the dispersion transversal index (DTI). From the measured parameters, an anisotropy index was derived, and the degree of anisotropy (DA) calculated with DLI and DTI. We have compared the results from the vertebral fracture cases and control cases. The best distinction was obtained for the largest ROI located in the great tuberosity of the calcaneus for all parameters ( P <10(-4)).( )The DA parameter showed a higher value in vertebral fracture cases (1.746+/-0.169) than in control cases (1.548+/-0.136); P <10(-4), and the difference persisted after removal of the subjects with hormonal replacement therapy. The analysis of the receiver operating characteristics (ROC) has shown the best results with DA and Hmean: areas under curves (AUCs) respectively of 0.765 and 0.683, while AUCs associated to LS-BMD and FN-BMD were 0.614 and 0.591 lower, respectively. We determined the odds ratios (OR) by uni- and multivariate analysis. Crude ORs were respectively 3.91 (95% CI: 2.22-6.87) and 3.08 (95% CI: 1.72-5.52) for DA and Hmean. Crude ORs were respectively 1.71 (95% CI: 1.15-2.56) and 1.56 (95% CI: 1.05-2.31) for LS-BMD and FN-BMD. All ORs were statistically significant, and those associated to Hmean and anisotropy indices were higher than those of BMD measurements. From a multivariate analysis including anisotropy indices, Hmean, age and FN-BMD, the remaining significant ORs were respectively 6.33 (95% CI: 2.80-14.30) and 3.08 (95% CI: 1.48-6.37) for DA and Hmean. These data have shown that anisotropy indices on calcaneus radiographs can distinguish vertebral fracture cases from control cases. This analysis provides complementary information concerning the BMD and fractal parameter. These data suggest that we can improve the fracture risk evaluation by adding information related to the directional organization of trabecular bone derived from the FFT spectrum on conventional radiographic images.