Field Imaging Research Articles

Prediction of Abnormal Growth of Breast Cancer Cell Using Support Vector Machine

Abstract: Breast cancer is a serious health concern that affects millions of women worldwide. Early detection of breast cancer is crucial for effective treatment and improved survival rates. Artificial intelligence (AI) has shown great promise in the field of medical imaging and has been increasingly used for breast cancer detection. Cancer is one of the most dangerous diseases to humans, and yet no permanent cure has been developed for it. Breast cancer is one of the most common cancer types. According to the National Breast Cancer foundation, in 2020 alone, more than 276,000 new cases of invasive breast cancer and more than 48,000 non-invasive cases were diagnosed in the US. To put these figures in perspective, 64% of these cases are diagnosed early in the disease’s cycle, giving patients a 99% chance of survival. In this paper we develop a system for diagnosis, prognosis and prediction of breast cancer using AI. This will assist the doctors in diagnosis of the disease. In this research work, we systematically reviewed previous work done on detection and treatment of breast cancer using genetic sequencing or histopathological imaging with the help of deep learning and machine learning We use of AI for breast cancer detection, including studies that have used mammography, ultrasound, and magnetic resonance imaging (MRI) as imaging modalities. AI can also improve the accuracy and efficiency of breast cancer screening and diagnosis, as well as the challenges that must be addressed to ensure the successful integration of AI into clinical practice. However, further research and development are needed to optimize AI algorithms and ensure their safety and effectiveness in clinical practice. Breast cancer detection has emerged as a critical area of research, leveraging the power of Artificial Intelligence (AI) to enhance diagnostic accuracy and efficiency. Utilizing AI technologies, particularly deep learning, enables the development of robust and precise models for early breast cancer detection. These models analyze mammographic images, identifying subtle patterns indicative of malignancies that may escape human eye detection. Transfer learning, employing pre-trained neural network architectures, has demonstrated significant success in leveraging existing knowledge for improved performance. The integration of convolutional neural networks (CNNs) and image processing techniques empowers AI models to discern subtle abnormalities, aiding in the timely identification of potential breast tumors. Additionally, AI-driven diagnostic tools can streamline radiologists' workflow, providing rapid and reliable assessments, ultimately contributing to early intervention and improved patient outcomes. As these technologies continue to evolve, the synergy between AI and breast cancer detection promises to revolutionize screening methods, fostering a paradigm shift towards more accurate, efficient, and accessible diagnostic practices in the realm of breast health.

On the bright-end of the UV luminosity functions of galaxies at z ∼ 0.6–1.2

Abstract We derive the Ultra-Violet (UV) luminosity function (LF) of star-forming galaxies in the redshift range z = 0.6 − 1.2, in the rest-frame far-UV (1500 Å) wavelength. For this work, we are in particular interested in the bright end of the UV LF in this redshift range. Data from the XMM-Newton Optical Monitor (XMM-OM), near-ultraviolet (2410–3565 Å) observations over 1.5 deg2 of the COSMOS field are employed for this purpose. We compile a source-list of 879 sources with UVW1AB in the range ∼21–24 mags from the wide-area UVW1 image of the COSMOS field in the two bins 0.6 ≤ z ≤ 0.8 and 0.8 ≤ z ≤ 1.2. The M1500 for these sources lies in the interval [ − 19.10, −22.50]. We use the maximum likelihood to fit a Schechter function model to the un-binned data to estimate the parameters (faint-end slope, characteristic magnitude, and normalisation) of the Schechter function. We find the shape of the LF to be consistent with the Schechter model, and the parameters are in fair agreement with other studies conducted using direct measurements of the 1500 Å flux. We see a brightening of the characteristic magnitude as we move from lower (0.7) to higher (1.0) redshift. The measures for luminosity density are within the error margins of past studies. We examine the brightest sources in our sample for the AGN contribution. These sources are characterised by their spectral energy distributions, integrated infrared luminosities, and morphologies. We also explore their overlap with the brightest IR galaxies in a similar redshift range.

Upgraded and Light-Up Biosensing Platform: Entropy-Driven Catalysis Circuit Manipulates the Configuration Transformation of Novel DNA Silver Nanoclusters on the Graphene Oxide Surface.

To tackle the predicament of the traditional turn-off mechanism, exploring an activated turn-on system remains an intriguing and crucial objective in biosensing fields. Herein, a dark DNA Ag nanocluster (NC) with hairpin-structured DNA containing a six-base cytosine loop (6C loop) as a template is atypically synthesized. Intriguingly, the dark DNA Ag NCs can be lit to display strong red-emission nanoclusters. Building upon these exciting findings, an unprecedented and upgraded turn-on biosensing system [entropy-driven catalysis circuit (EDCC)-Ag NCs/graphene oxide (GO)] has been created, which employs an EDCC to precisely manipulate the conformational transition of DNA Ag NCs on the GO surface from adsorption to desorption. Benefiting from the effective quenching of GO and signal amplification capability of the EDCC, the newly developed EDCC-Ag NCs/GO biosensing system displays a high signal-to-background (S/B) ratio (26-fold) and sensitivity (limit of detection as low as 0.4 pM). Meanwhile, it has good specificity, excellent stability, and reliability in both buffer and biological samples. To the best of our knowledge, it is the first example that adopts an EDCC to precisely modulate the configuration transformation of DNA Ag NCs on the GO surface to obtain a biosensor with low background, strong fluorescence, high contrast, and sensitivity. This exciting finding may provide a new route to fabricate a novel turn-on biosensor based on hairpin-templated DNA Ag NCs in the optical imaging and bioanalytical fields.

CSST Dense Star Field Preparation: A Framework for Astrometry and Photometry for Dense Star Field Images Obtained by the China Space Station Telescope (CSST)

Abstract The Chinese Space Station Telescope (CSST) is a telescope with 2-meter diameter, obtaining images with high quality through wide-field observations. In its first observation cycle, the CSST will scan portions of the galactic centre with 7 different bands across different epochs to capture time-domain observation data. These data have significant potential for the study of properties of stars and exoplanets. However, the density of stars in the galactic centre is high, and it is a well-known challenge to perform astrometry and photometry in such a dense star field. This paper presents a deep learning-based framework designed to process dense star field images obtained by the CSST, which includes photometry, astrometry, and classifications of targets according to their light curve periods. With simulated CSST observation data, we demonstrate that this deep learning framework achieves photometry accuracy of 0.23% and astrometry accuracy of 0.03 pixel for stars with moderate brightness mag=24 in i band, surpassing results obtained by traditional methods. Additionally, the
deep learning based light curve classification algorithm could pick up celestial targets whose magnitude variations are 1.7 times larger than magnitude variations brought by Poisson Photon Noise. We anticipate that our framework could be effectively used
to process dense star field images obtained by the CSST.

Digital histological staining of tissue slide images from optical coherence microscopy

The convergence of staining-free optical imaging and digital staining technologies has become a central focus in digital pathology, presenting significant advantages in streamlining specimen preparation and expediting the rapid acquisition of histopathological information. Despite the inherent merits of optical coherence microscopy (OCM) as a staining-free technique, its widespread application in observing histopathological slides has been constrained. This study introduces a novel approach by combining wide-field OCM with digital staining technology for the imaging of histopathological slides. Through the optimization of the histology slide production process satisfying the ground growth for digital staining as well as pronounced contrast for OCM imaging, successful imaging of various mouse tissues was achieved. Comparative analyses with conventional staining-based bright field images were executed to evaluate the proposed methodology's efficacy. Moreover, the study investigates the generalization of digital staining color appearance to ensure consistent histopathology, considering tissue-specific and thickness-dependent variations.

Near‐Infrared Metalens Empowered Dual‐Mode High Resolution and Large FOV Microscope

AbstractThe spiral phase contrast microscope can clearly distinguish the morphological information of the low contrast objects (i.e., biological samples) owing to the isotropic edge‐enhancement effect, while the bright field microscope can image the overall morphology of amplitude objects. However, the imaging resolution, magnification, and field of view of conventional spiral phase contrast microscopes based on 4f filtering configuration are limited by the system's complexity. Here, compact dual‐mode microscopes working at near‐infrared using the engineered metalens are reported, which can be tuned between the spiral phase contrast imaging and bright field imaging by polarization control. The metalens combine the high‐resolution objective lens and polarization‐controlled phase filter into a single‐layer nanofins array. Two infinity‐corrected microscope systems are demonstrated to achieve subwavelength resolution (0.7λ), large magnification (58X), and large field of view (600 × 800 µm). Unstained onion epidermal is imaged by the microscope to show the dual‐mode imaging ability for the biological sample. Finally, a singlet dual‐mode microscope system is demonstrated to show the edge‐detection application for industrial standards. These results can open new opportunities in applications of biological imaging, industrial machine vision, and semiconductor inspection.

Light field image super-resolution based on dual learning and deep Fourier channel attention

Light field (LF) imaging has gained significant attention in the field of computational imaging due to its unique capability to capture both spatial and angular information of a scene. In recent years, super-resolution (SR) techniques based on deep learning have shown considerable advantages in enhancing LF image resolution. However, the inherent challenges of obtaining rich structural information and reconstructing complex texture details persist, particularly in scenarios where spatial and angular information are intricately interwoven. This Letter introduces a novel, to the best of our knowledge, approach for Disentangling LF Image SR Network (DLISN) by leveraging the synergy of dual learning and Fourier channel attention (FCA) mechanisms. Dual learning strategies are employed to enhance reconstruction results, addressing limitations in model generalization caused by the difficulty in acquiring paired datasets in real-world LF scenarios. The integration of FCA facilitates the extraction of high-frequency information associated with different structures, contributing to improved spatial resolution. Experimental results consistently demonstrate superior performance in enhancing the resolution of LF images.

UFBSM: unmixing fusion and background sparse dictionary Model for hyperspectral anomaly detection

ABSTRACT In recent years, low-rank sparse representation (LRR) schemes have been applied to the field of hyperspectral image (HSI) anomaly detection (AD) and received attention. However, it is often accompanied by a small amount of noise in the AD, which leads to a high false alarm rate of detection results. In this paper, the HSI-AD algorithm is proposed to address this problem. First, the HSI is unmixed using the deep network to obtain the endmembers and abundance, and the abundance matrix is fused with the HSI to obtain the mixed data, which to a certain extent complements the spatial information of the HSI because the abundance matrix can reflect the subpixel-level information of the target image elements. Then, the mixed data are decomposed based on the model proposed in this paper. To overcome the situation that the sparse part of the traditional model is prone to mix with a small amount of noise, the sparse decomposition of the mixed data into anomaly and noise is used to remove the small amount of noise from the sparse part. A joint dictionary consisting of background and anomaly is used in the above model. In addition, to address the problem of information loss caused by the uniform accumulation of kernel norm over all singular values in the process of model decomposition, this paper designs the γ norm decomposition method to replace the general kernel norm decomposition for a more effective extraction of useful information.

Including gender dimension in operations management teaching

Purpose: This paper aims at introducing the importance and the relevant concepts and tools for including gender dimension in teaching, with a special focus in the Operations Management knowledge field.Design/methodology/approach: The methodology is based on a literature review and on the knowledge and experience of the authors in teaching in the field of Operations Management and designing and implementing trainings and pilot programs for including gender dimension in teaching and.Findings: Even if there already exist some general guidelines for including gender dimension in teaching and in some countries the law requires the inclusion of gender dimension in high education, the effective inclusion of gender dimension in teaching is still in a very preliminary stage. Moreover, there are not specific studies focused on the Operations Management field.Research limitations/implications: Even if the authors have wide experience in the field, the proposals have not been applied to many courses so there is no empirical evidence of the achieved results.Practical implications: The paper can be useful for people teaching in Industrial Engineering and Operations Management courses’, as it gives not only some general guidelines and concepts but also specific examples that can be directly applied to their courses.Social implications: Including gender dimension in Industrial Engineering and Operations Management courses, which are still male dominated, can help achieving the SDG 5, which is focused on achieving gender equality and empowering all women and girls. Specifically, including gender dimension in teaching allow breaking stereotypes, changing the image of the knowledge field, empowering female students and giving both men and women tools for including the gender dimension in their professional careers.Originality/value: There exist some general guidelines for including gender dimension in teaching but, to the best of our knowledge, there is no any specific work that is focused on the Operations Management knowledge field.

Quantitative phase image stitching guided by reconstructed intensity images in one-shot double field of view multiplexed digital holographic microscopy

In digital holographic microscopy (DHM), achieving large field of view (FOV) imaging while maintaining high resolution is critical for quantitative phase measurements of biological cell tissues and micro-nano structures. We present a quantitative phase image stitching guided by reconstructed intensity images in one-shot double FOV multiplexed DHM. Double FOVs are recorded simultaneously through frequency division multiplexing; intensity feature pairs are accurately extracted by multi-algorithm fusion; aberrations and non-common baselines are effectively corrected by preprocessing. Experimental results show that even if phase images have coherent noise, complex aberrations, low overlap rate and large size, this method can achieve high-quality phase stitching.

Role of radiomics as a predictor of disease recurrence in ovarian cancer: a systematic review.

Ovarian cancer is associated with high cancer-related mortality rate attributed to late-stage diagnosis, limited treatment options, and frequent disease recurrence. As a result, careful patient selection is important especially in setting of radical surgery. Radiomics is an emerging field in medical imaging, which may help provide vital prognostic evaluation and help patient selection for radical treatment strategies. This systematic review aims to assess the role of radiomics as a predictor of disease recurrence in ovarian cancer. A systematic search was conducted in Medline, EMBASE, and Web of Science databases. Studies meeting inclusion criteria investigating the use of radiomics to predict post-operative recurrence in ovarian cancer were included in our qualitative analysis. Study quality was assessed using the QUADAS-2 and Radiomics Quality Score tools. Six retrospective studies met the inclusion criteria, involving a total of 952 participants. Radiomic-based signatures demonstrated consistent performance in predicting disease recurrence, as evidenced by satisfactory area under the receiver operating characteristic curve values (AUC range 0.77-0.89). Radiomic-based signatures appear to good prognosticators of disease recurrence in ovarian cancer as estimated by AUC. The reviewed studies consistently reported the potential of radiomic features to enhance risk stratification and personalise treatment decisions in this complex cohort of patients. Further research is warranted to address limitations related to feature reliability, workflow heterogeneity, and the need for prospective validation studies.

Low-sampling high-quality Hadamard and Fourier single-pixel imaging through automated optimization neural network

In this manuscript, an automated optimization neural network is applied in Hadamard single-pixel imaging (H-SPI) and Fourier single-pixel imaging (F-SPI) to improve the imaging quality at low sampling ratios which is called AO-Net. By projecting Hadamard or Fourier basis illumination light fields onto the object, a single-pixel detector is used to collect the reflected light intensities from object. The one-dimensional detection values are fed into the designed AO-Net, and the network can automatically optimize. Finally, high-quality images are output through multiple iterations without pre-training and datasets. Numerical simulations and experiments demonstrate that AO-Net outperforms other existing widespread methods for both binary and grayscale images at low sampling ratios. Specially, the Structure Similarity Index Measure value of the binary reconstructed image can reach more than 0.95 when the sampling ratio is less than 3%. Therefore, AO-Net holds great potential for applications in the fields of complex environment imaging and moving object imaging.

Multiscale 3D displacement field measurement using stereo digital image correlation on a fractal speckle pattern

AbstractEven though the simulations used to predict failure are becoming increasingly predictive, complex multiaxial loading tests are still required to validate the design of structural components in a wide range of industries. Large specimen testing often requires two different scales. A global Far Field to obtain boundary conditions and a local Near Field to evaluate strain gradients around discontinuities such as bolts, notches… The main goal of this study is to provide a continuous displacement over the whole specimen surface integrating data from multiple cameras. In this paper, we propose a new methodology that generates 3D displacements determined by finite‐element stereo digital image correlation in the Near Field and in the Far Field using a unique fractal speckle pattern and an off‐line determined texture. The displacements are obtained in the same coordinate system and on the same mesh. Satisfactory data fusion from both Near Field and Far Field images of a biaxial test on a notched laminate composite was obtained with a refined mesh at the notch tip. This methodology can be applied to any tests requiring multiple camera systems and will support the use of the finite‐element digital image correlation framework as an experimental‐numerical efficient technique.

Gravity and Magnetic Joint Imaging Based on Gramian Constraints

Gravity and magnetic imaging produce numerical images proportional to the density and magnetization source distribution through the upward continuation of potential field data. However, the inherent ambiguity and non-uniqueness in gravity or magnetic imaging restrict the reliability of the imaging model. We develop a joint iterative imaging framework for gravity and magnetic data based on Gramian constraints. The imaging fields of gravity and magnetic field are initially calculated by the depth from extreme points method. With the recovered magnetization and density distribution, the gradient directions of the Gramian function for these model parameters are calculated and utilized to regularize the joint imaging directions for gravity and magnetic fields. The introduction of Gramian constraints achieves mutual complementarity of the gravity and magnetic data and generates density and magnetization models with more distinct boundaries and improved structural coherence. The proposed approach is tested by two synthetic examples and applied to field data from the Galinge iron deposit in Qinghai, China. The real case results are verified by information from drillholes and physical properties measurements of ore and rock samples. Joint imaging provides an alternative to joint inversion with improved resolution and reliability of imaging models compared to separate imaging through the complementarity of gravity and magnetic data.

Roadmap on nanoscale magnetic resonance imaging.

The field of nanoscale magnetic resonance imaging (NanoMRI) was started 30 years ago. It was motivated by the desire to image single molecules and molecular assemblies, such as proteins and virus particles, with near-atomic spatial resolution and on a length scale of 100 nm. Over the years, the NanoMRI field has also expanded to include the goal of useful high-resolution nuclear magnetic resonance (NMR) spectroscopy of molecules under ambient conditions, including samples up to the micron-scale. The realization of these goals requires the development of spin detection techniques that are many orders of magnitude more sensitive than conventional NMR and MRI, capable of detecting and controlling nanoscale ensembles of spins. Over the years, a number of different technical approaches to NanoMRI have emerged, each possessing a distinct set of capabilities for basic and applied areas of science. The goal of this roadmap article is to report the current state of the art in NanoMRI technologies, outline the areas where they are poised to have impact, identify the challenges that lie ahead, and propose methods to meet these challenges. This roadmap also shows how developments in NanoMRI techniques can lead to breakthroughs in emerging quantum science and technology applications.&#xD.

Generating real-scene hologram through light field imaging and deep learning

Abstract Addressing the challenge of acquiring holograms from real-world scenes, this study introduces a novel approach leveraging light field cameras to capture light field data, which is subsequently transformed into authentic scene holograms. This methodology integrates light field imaging technology with a pre-trained deep neural network. To compensate for the limitations inherent in camera hardware, a super-resolution algorithm is employed. The conversion of light field information into RGB-D data facilitates its input into the deep neural network, enabling the inference of corresponding real-world scene holograms. Empirical evidence demonstrates that the system is capable of inferring high-resolution (1920×1080) real-world scene holograms within a timeframe of 5 seconds, utilizing hardware comprising an NVIDIA RTX 3060.

Causal mediation analysis with a three-dimensional image mediator.

Causal mediation analysis is increasingly abundant in biology, psychology, and epidemiology studies and so forth. In particular, with the advent of the big data era, the issue of high-dimensional mediators is becoming more prevalent. In neuroscience, with the widespread application of magnetic resonance technology in the field of brain imaging, studies on image being a mediator emerged. In this study, a novel causal mediation analysis method with a three-dimensional image mediator is proposed. We define the average casual effects under the potential outcome framework, explore several sufficient conditions for the valid identification, and develop techniques for estimation and inference. To verify the effectiveness of the proposed method, a series of simulations under various scenarios is performed. Finally, the proposed method is applied to a study on the causal effect of mother's delivery mode on child's IQ development. It is found that cesarean section may have a negative effect on intellectual performance and that this effect is mediated by white matter development. Additional prospective and longitudinal studies may be necessary to validate these emerging findings.

Spectroscopy Detection and Imaging System Based on Line Array Single Photon Detectors

Abstract. Single-photon detectors, with their exceptional sensitivity, provide a reliable means for single-photon-level detection, demonstrating significant advantages in detecting weak signals in complex environments compared to traditional detectors. With the continuous advancement in semiconductor manufacturing technology, single-photon detectors based on linear array configurations have emerged and rapidly developed. This study utilizes a linear array single-photon detector in free-running mode combined with a scanning mechanism to design and implement a spectral detection and imaging system. Through the spectral scanning unit, this system successfully achieves precise spectral detection in the 890 nm to 1710 nm range, with a spectral resolution better than 2 nm. Utilizing the imaging scanning unit, the system effectively performs target spectral imaging under single and multiple wavelength conditions at 1064 nm, 1310 nm, and 1520 nm. By optimizing algorithms for data processing, the system can achieve rapid and accurate spectral detection and imaging even under low-light conditions where the average photon count per pixel is less than 3. The results of this study are expected to provide strong technical support for the application of spectral imaging technology in the field of high-speed detection and imaging.

Effect of plate orientation on apparent thickness of mineral plates by transmission electron microscopy.

Transmission electron microscopy (TEM) is widely used to study the ultrastructure of bone. The mineral of bone occurs as polycrystalline mineral plates about 3 to 6 nm in thickness. A problem in using TEM to make quantitative analyses of bone is that the orientation of the plates with respect to the plane of the section being imaged is expected to affect their apparent thickness. The purpose of this study was to test if this was true, if the apparent thickness of plates changed substantially as a result of tilt of the section. We prepared TEM sections of samples of cortical human bone by ion beam milling, orienting one section parallel to the collagen fibril axes and one perpendicular to them. We obtained TEM bright field and HAADF images of these sections, tilting the sections up to ± 20° at 2° intervals and measuring the apparent thickness of individual mineral platelets at each angle of tilt. Thickness appears to double as section is tilted ± 20°. True thickness of plates is determined by tilting the section along an axis parallel to the plate orientation and determining the minimum apparent thickness. However, as plates are tilted away from minimum-thickness orientation, they become less well-resolved, disappearing when tilted more than 20°. We therefore also measured apparent thickness of only the darkest (most electron scattering) plate images in an untilted section and obtained the same average thickness as that obtained by tilting. We conclude that tilting of the section is not necessary to obtain an accurate measurement of the thickness of mineral plates.

Three-dimensional electrochemical-magnetic-thermal coupling model for lithium-ion batteries and its application in battery health monitoring and fault diagnosis

Storage batteries with elevated energy density, superior safety and economic costs continues to escalate. Batteries can pose safety hazards due to internal short circuits, open circuits and other malfunctions during usage, hence real-time surveillance and error diagnosis of the battery’s operational state is imperative. In this paper, a three-dimensional model of electrochemical-magnetic field-thermal coupling is formulated with lithium-ion pouch cells as the research focus, and the spatial distribution pattern of the physical field such as magnetic field and temperature when the battery is operational is acquired. Furthermore, this manuscript also investigates the diagnostic methodology for defective batteries with internal short circuits and fissures, that is, the operational state of the battery is evaluated and diagnosed by the distribution of the magnetic field surrounding the battery. To substantiate the method’s practical viability, the present study extends its examination to the 18650-battery pack. We obtained the magnetic field images of the normal operation of the battery pack and the failure state of some batteries and analyzed the relationship between the magnetic field distribution characteristics and the performance of the battery pack, providing a new method for the health monitoring and fault diagnosis of the battery pack. This non-contact method incurs no damage to the battery, concurrently exhibiting elevated sensitivity and extremely rapid response time. Meanwhile, it provides an effective means for non-destructive research on the batteries and can be applied to areas such as battery safety screening and non-destructive testing. This research not only helps to facilitate our understanding of the battery’s operating mechanism, but also provides robust support for safe operation and optimal battery design.