Predicting Detection Performance on Security X-Ray Images as a Function of Image Quality

Visualized X-ray Dosimetry for Multienvironment Applications

X-ray dose detection plays a critical role in various scientific fields, including chemistry, materials, and medicine. However, the current materials used for this purpose face challenges in both immediate and delayed radiation detections. Here, we present a visual X-ray dosimetry method for multienvironment applications, utilizing NaLuF4 nanocrystals (NCs) that undergo a color change from green to red upon X-ray irradiation. By adjustment of the concentrations of Ho3+, the emission color of the NCs can be tuned thanks to the cross-relaxation effects. Furthermore, X-ray irradiation induces generation of trapping centers in NaLuF4:Ho3+ NCs, endowing the generation of mechanoluminescence (ML) behavior upon mechanical stimulation after X-ray irradiation ceases. The ML intensity shows a linear correlation with the X-ray dose, facilitating the detection of delayed radiation. This breakthrough facilitates X-ray dose inspection in flaw detection, nuclear medicine, customs, and civil protection, thereby enhancing opportunities for radiation monitoring and control.

Enhanced detonators detection in X-ray baggage inspection by image manipulation and deep convolutional neural networks

Detecting detonators is a challenging task because they can be easily mis-classified as being a harmless organic mass, especially in high baggage throughput scenarios. Of particular interest is the focus on automated security X-ray analysis for detonators detection. The complex security scenarios require increasingly advanced combinations of computer-assisted vision. We propose an extensive set of experiments to evaluate the ability of Convolutional Neural Network (CNN) models to detect detonators, when the quality of the input images has been altered through manipulation. We leverage recent advances in the field of wavelet transforms and established CNN architectures-as both of these can be used for object detection. Various methods of image manipulation are used and further, the performance of detection is evaluated. Both raw X-ray images and manipulated images with the Contrast Limited Adaptive Histogram Equalization (CLAHE), wavelet transform-based methods and the mixed CLAHE RGB-wavelet method were analyzed. The results showed that a significant number of operations, such as: edges enhancements, altered color information or different frequency components provided by wavelet transforms, can be used to differentiate between almost similar features. It was found that the wavelet-based CNN achieved the higher detection performance. Overall, this performance illustrates the potential for a combined use of the manipulation methods and deep CNNs for airport security applications.

Multimodal intrinsic speckle-tracking (MIST) to extract images of rapidly-varying diffuse X-ray dark-field

Speckle-based phase-contrast X-ray imaging (SB-PCXI) can reconstruct high-resolution images of weakly-attenuating materials that would otherwise be indistinguishable in conventional attenuation-based X-ray imaging. The experimental setup of SB-PCXI requires only a sufficiently coherent X-ray source and spatially random mask, positioned between the source and detector. The technique can extract sample information at length scales smaller than the imaging system's spatial resolution; this enables multimodal signal reconstruction. "Multimodal Intrinsic Speckle-Tracking" (MIST) is a rapid and deterministic formalism derived from the paraxial-optics form of the Fokker-Planck equation. MIST simultaneously extracts attenuation, refraction, and small-angle scattering (diffusive dark-field) signals from a sample and is more computationally efficient compared to alternative speckle-tracking approaches. Hitherto, variants of MIST have assumed the diffusive dark-field signal to be spatially slowly varying. Although successful, these approaches have been unable to well-describe unresolved sample microstructure whose statistical form is not spatially slowly varying. Here, we extend the MIST formalism such that this restriction is removed, in terms of a sample's rotationally-isotropic diffusive dark-field signal. We reconstruct multimodal signals of two samples, each with distinct X-ray attenuation and scattering properties. The reconstructed diffusive dark-field signals have superior image quality-as measured by the naturalness image quality evaluator, signal-to-noise ratio, and azimuthally averaged power-spectrum-compared to our previous approaches which assume the diffusive dark-field to be a slowly varying function of transverse position. Our generalisation may assist increased adoption of SB-PCXI in applications such as engineering and biomedical disciplines, forestry, and palaeontology, and is anticipated to aid the development of speckle-based diffusive dark-field tensor tomography.

Evaluation of a no-reference image quality metric for projection X-ray imaging using a 3D printed patient-specific phantom

PURPOSE

Feasability of a no-reference image quality metric was assessed on patient-like images using a patient-specific phantom simulating a frame of a coronary angiogram.

METHODS

One background and one contrast-filled frame of a coronary angiogram, acquired using a clinical imaging protocol, were selected from a Philips Integris Allura FD (Philips Healthcare, Best, The Netherlands). The background frame's pixels were extruded to a thickness proportional to their grey value. One phantom was 3D printed using composite 80% bronze filament (max. thickness of 5.1 mm), the other was a custom PMMA cast (max thickness of 8.5 cm). A vessel mold was created from the contrast-filled frame and injected with a solution of 320 mg I/ml contrast fluid (75%), water and gelatin. Still X-ray frames of the vessel mold + background phantom + 16 cm PMMA were acquired at manually selected different exposure settings using a Philips Azurion (Philips Healthcare, Best, The Netherlands) in User Quality Control Mode and were exported as RAW images. The signal-difference-to-noise-ratio-squared (SDNR²) and a spatial-domain-equivalent of the noise equivalent quanta (NEQ_SDE) were calculated. The Spearman's correlation of the latter parameters with a no-reference perceptual image quality metric (NIQE) was investigated.

RESULTS

The bronze phantom showed better resemblance to the original patient frame selected from a coronary angiogram of an actual patient, with better contrast and less blur than the PMMA phantom. Both phantoms were imaged using a comparable imaging protocol to the one used to acquire the original frame. The bronze phantom was hence used together with the vessel mold for image quality measurements on the 165 still phantom frames. A strong correlation was noted between NEQ_SDE and NIQE (SROCC = -0.99, p < 0.0005) and between SDNR² and NIQE (SROCC = -0.97, p < 0.0005).

CONCLUSION

Using a cost-effective and easy to realize patient-specific phantom we were able to generate patient-like X-ray frames. NIQE as a no-reference image quality model has the potential to predict physical image quality from patient images.

A Novel Symmetry Driven Siamese Network for THz Concealed Object Verification

Security inspection aims to improve the high detection rate as well as reduce the false alarm rate. However, it still suffers from two challenges affecting its robustness. 1) Existing security inspection methods are mostly designed for natural images, which cannot reflect the uniqueness and imaging principle of THz images. 2) Existing methods is sensitive to noise interference and pose variations. This work revisits these challenges and presents a novel symmetry driven Siamese network (SDSN) for THz concealed object verification. Our idea is to employ a specially designed network architecture for THz concealed object verification. First, to reflect the uniqueness and the special property of THz images, Siamese network with Contrastive loss is used for feature extraction along with symmetrical prior information consideration, which can learn symmetrical metrics from the same person. Second, to alleviate the impact of noise interference and pose variations, the adaptive identity normalization (A-IDN) is proposed to normalize the symmetrical metrics each person. Finally, to enhance the generalization of network, an adaptive selective threshold based on Gaussian mixture model (AST-GMM) is designed, which serves as a classifier for the final classification results. Extensive experiments show that SDSN significantly improves the accuracy. Specially, SDSN outperforms the state-of-the-art methods without symmetrical prior information on THz security dataset.

Perceptual Quality Assessment of Pan-Sharpened Images

Pan-sharpening (PS) is a method of fusing the spatial details of a high-resolution panchromatic (PAN) image with the spectral information of a low-resolution multi-spectral (MS) image. Visual inspection is a crucial step in the evaluation of fused products whose subjectivity renders the assessment of pansharpened data a challenging problem. Most previous research on the development of PS algorithms has only superficially addressed the issue of qualitative evaluation, generally by depicting visual representations of the fused images. Hence, it is highly desirable to be able to predict pan-sharpened image quality automatically and accurately, as it would be perceived and reported by human viewers. Such a method is indispensable for the correct evaluation of PS techniques that produce images for visual applications such as Google Earth and Microsoft Bing. Here, we propose a new image quality assessment (IQA) measure that supports the visual qualitative analysis of pansharpened outcomes by using the statistics of natural images, commonly referred to as natural scene statistics (NSS), to extract statistical regularities from PS images. Importantly, NSS are measurably modified by the presence of distortions. We analyze six PS methods in the presence of two common distortions, blur and white noise, on PAN images. Furthermore, we conducted a human study on the subjective quality of pristine and degraded PS images and created a completely blind (opinion-unaware) fused image quality analyzer. In addition, we propose an opinion-aware fused image quality analyzer, whose predictions with respect to human perceptual evaluations of pansharpened images are highly correlated.

Multivariate Statistical Approach to Image Quality Tasks

Many existing Natural Scene Statistics-based no reference image quality assessment (NR IQA) algorithms employ univariate parametric distributions to capture the statistical inconsistencies of bandpass distorted image coefficients. Here we propose a multivariate model of natural image coefficients expressed in the bandpass spatial domain that has the potential to capture higher-order correlations that may be induced by the presence of distortions. We analyze how the parameters of the multivariate model are affected by different distortion types, and we show their ability to capture distortion-sensitive image quality information. We also demonstrate the violation of Gaussianity assumptions that occur when locally estimating the energies of distorted image coefficients. Thus we propose a generalized Gaussian-based local contrast estimator as a way to implement non-linear local gain control, that facilitates the accurate modeling of both pristine and distorted images. We integrate the novel approach of generalized contrast normalization with multivariate modeling of bandpass image coefficients into a holistic NR IQA model, which we refer to as multivariate generalized contrast normalization (MVGCN). We demonstrate the improved performance of MVGCN on quality relevant tasks on multiple imaging modalities, including visible light image quality prediction and task success prediction on distorted X-ray images.