Articles tagged with Image Processing
The review highlights advances in fundamental visualization methods for medical images in 3D, including scalar, vector, and tensor data. Medical professionals can quickly locate proper techniques using a taxonomy of medical problems and examples of health applications.
A new computer-aided diagnostic tool, Deep-Lung Parenchyma-Enhancing (DLPE), uses artificial intelligence to reveal signs of pulmonary fibrosis in COVID long-haulers, helping to explain respiratory symptoms and improve disease management.
Researchers developed a system to estimate the probability of a majority response being wrong, eliminating unnecessary task assignments and increasing efficiency in crowdsourcing projects. This method can be applied to various binary classification tasks, reducing labor and improving accuracy.
An artificial intelligence model trained on histological images of surgical specimens accurately classified patients with and without Crohn disease recurrence. The model revealed previously unrecognized differences in adipose cells and mast cell infiltration, enabling stratification by prognosis for postoperative Crohn disease patients.
Artificial Intelligence can now identify legendary batting techniques used by Sir Donald Bradman and modern players. Researchers developed a deep learning computer vision AI model to detect lateral backlift batters from straight ones.
Scientists have developed a transparent device that produces a hidden image when light shines on it, using liquid crystals to recreate an ancient light trick. The technology has the potential to enable reconfigurable displays and stable 3D images.
Researchers found that speeding can decrease fuel economy by 7% and result in an extra 28 cents per gallon at current US fuel prices. They also studied human behavior during the early days of COVID-19 and simulated cosmic collisions to better understand X-ray emissions.
Researchers have developed a novel image reconstruction method using Vision Transformer (ViT) architecture to overcome limitations of conventional methods. The proposed algorithm enables the acquisition of high-quality images in a short computing time, suitable for real-time capture and various applications.
Researchers used terahertz imaging to uncover a hidden inscription on a 16th-century lead funerary cross, revealing the Lord's Prayer. The technique allowed for non-destructive examination of the corrosion layer, enabling the team to restore and enhance images containing the text.
Researchers developed a live imaging system to observe collagen synthesis in fibroblasts, revealing the intracellular processing and transportation of collagen fibers. The study found that this step controls the speed of collagen synthesis, providing a new understanding of collagen production.
Two new methods for producing high-resolution visualizations of small artefacts are presented, allowing anyone to create high-quality images and models with minimal effort and cost. The protocols provide detailed workflows for photographic acquisition and processing, enabling replicability and reproducibility in the field of archaeology.
A team of researchers developed a novel chip-based infection model to study invasive aspergillosis, a mold infection that affects the lungs. The model allows for live microscopic observation of damage caused by fungal hyphae and the response of immune cells.
A novel method developed by the University of Tsukuba uses drones and machine learning to estimate the amount of plastic litter in rivers. The approach combines high-resolution optical and thermal images, resulting in more accurate estimates than other methods.
Researchers developed a novel algorithm, 'Joint Space and Frequency Reconstruction' (JSFR-SIM), to accelerate image reconstruction in optically sectioned superresolution structured illumination microscopy. The method achieves 80 times faster execution speed without compromising image quality.
Scientists develop models that complement simulations using reinforcement learning and numerical methods to predict climate change, turbulent flows, and morphogenesis. This approach enables faster and more energy-efficient predictions, solving complex problems in engineering and climate applications.
Researchers at Kyoto University found that chimpanzees exhibit a preference for faces and skulls of their own species, similar to previous studies with African elephants. This suggests the presence of a face module in chimpanzee brains that detects and processes facial cues.
Scientists have discovered a shapeshifting volcano virus with remarkable properties that let it alter its shape. This finding could lead to new ways to deliver drugs and vaccines, with implications for understanding how viruses evolved and potentially creating new technologies.
A team of researchers has developed a MEMS scanning lidar that can detect objects reliably even in shaky environments. The long-range MEMS lidar prototype uses a digital controller to suppress errors caused by vibrations, allowing for stable 3D imaging and object detection.
Mayo Clinic researchers propose a novel computational model that links Alzheimer's disease symptoms to brain anatomy, revealing a complex relationship between mental processing and brain function. The model uses machine learning to analyze patient brain imaging data and compresses complex brain anatomy into a conceptual framework.
Researchers at Nara Institute of Science and Technology developed a new calibration method to compensate for variations in illumination while scanning stained-glass windows. This approach can help capture the colors of cultural artifacts in a lifelike way, even with long scanning times.
Researchers warn of machine learning bias when data published for one task is used to train algorithms for a different one. This can lead to compromised integrity and 'overly optimistic' results in medical imaging applications.
Australian researchers have mapped the visual systems of hoverflies to detect drones' acoustic signatures, showing a 30-49% improvement in detection rates compared to traditional methods. The technology has potential applications for aviation safety and combatting IED-carrying drones.
Researchers at Kaunas University of Technology improved an algorithm to detect Alzheimer's disease from MRI images, achieving over 98% accuracy. The new model uses a modified neural network and adapts to variations in data, such as differences in hospital equipment and patient positions.
The new device, Bio-FlatScope, uses a custom algorithm to reconstruct images of micron-scale targets like cells and blood vessels inside the body. The light captured by Bio-FlatScope can be refocused after the fact to reveal 3D details, making it potentially valuable for detecting cancer or sepsis.
Researchers at Bielefeld University have identified five key characteristics of mitosis in the microalga Volvox carteri, including a porous nuclear envelope and crucial centrosome function. They used confocal laser scanning microscopy to capture high-resolution images of live cell division and gain insights into the complex process.
Researchers are developing a new SWIR surgical microscope system to detect and remove cholesteatomas, a type of chronic otitis media. The microscope uses short-wave infrared light to illuminate blood, bacterial biofilms, cartilage, and soft tissue, making them distinguishable from each other.
A new fluorescent DNA label has been developed to visualize disrupted DNA architecture in cancer cells, with promising results for improved cancer diagnoses and risk stratification. The study showed that the label can distinguish normal tissue from precancerous and cancerous lesions.
Adversarially robust models capture aspects of human peripheral processing, with results showing similarity in image transformations and perception alignment. The study's findings shed light on the goals of peripheral processing in humans and could help improve machine learning models.
The KAUST team has created a flexible and efficient scintillation film using lead-free metal halides, detecting X-rays at levels 113 times lower than standard medical imaging doses. This breakthrough enhances medical, industrial and security X-ray imaging, offering significant improvements in spatial resolution.
Scientists observe atomic magnetic field and origin of magnetism in iron atoms using Magnetic-field-free Atomic-Resolution STEM (MARS) and Differential Phase Contrast (DPC) method. This breakthrough enables research and development of various magnetic materials and devices.
Developed by University of Seville researchers, the new methodology has a sensitivity of 100% and specificity of 87.5%. It can detect SARS-CoV-2 in saliva and synthetic viruses with minimal equipment and training.
A new dataset from NYU Tandon School of Engineering and Woven Planet Holdings promises to help visually impaired pedestrians and autonomous vehicles navigate complex urban settings. The robust dataset uses over 200,000 outdoor images to test visual place recognition technologies that can improve navigation accuracy.
A new microscope allows for real-time aberration-free dynamic speckle microscopy using compressed time-reversal matrix technology. This enables almost real-time volumetric adaptive optical imaging with reduced data acquisition time and improved lateral resolution.
Researchers found lower functional connectivity between brain areas involved in social and emotional processing in individuals at risk of developing alcohol use disorder. This impairment may affect their ability to interpret facial expressions and respond to their environment, increasing the likelihood of disordered drinking.
A new study found that children's face-perception abilities are impaired when wearing masks, with a 20% impairment rate compared to adults' 15%. This disruption can affect social interactions and relationships. Researchers highlight the need for future studies on the impact of mask-wearing on children's educational performance.
Researchers at the University of Groningen have developed an AI system that can recognize indoor spaces with high accuracy by combining image and audio data. The system achieved a 70% accuracy rate in recognizing nine different types of indoor spaces, surpassing previous results.
Researchers from KTU proposed a deep-learning-based method for 3D human shape reconstruction using limited-angle depth data. The method can be integrated with existing virtual reality tools and has potential applications in telemedicine and remote diagnostics.
A Bar-Ilan University study found that participants remembered large images 1.5 times more than small ones, regardless of detail or resolution. This phenomenon may affect screen quality and learning on smartphones, suggesting larger screens could be better for studying.
A team of researchers from the University of Groningen developed an AI-based system that can identify individual Holstein cows in a milking station based on their coat pattern. The system achieved a recognition rate of 99.7% and has several advantages, including non-invasiveness, cost-effectiveness, and scalability.
A new high-speed projector can project RGB images and invisible infrared images simultaneously and independently at a rate of almost 1,000 fps. This technology has vast potential for applications such as dynamic projection mapping, which requires real-time image control to match complex moving targets.
Researchers at Duke University developed a holographic system that can image and analyze tens of thousands of cells per minute to spot signs of disease. The technique distinguished between healthy samples and cancerous or pre-cancerous cells with nearly 100% accuracy, using just four basic cellular physical parameters.
The new system can produce high-quality images comparable to those of conventional cameras, with a compact design suitable for minimally invasive endoscopy and full-scene sensing. This breakthrough could revolutionize medical imaging and robotics with size and weight constraints.
A study has developed a method using dark-field microscopy and deep learning algorithms to identify microplastics in human cells, achieving an accuracy of 93% for 1-micron polystyrene particles. The technique has the potential to screen microplastics in various samples, reducing time-consuming data acquisition and processing steps.
A new study published in Frontiers in Psychology has identified specific facial features that can be used to distinguish children's faces from adult faces. These features include the shape of the nose and eyebrows for adult faces and the eye, jawline, and nasal bone for child faces.
A new virtual histology technology may reduce the need for invasive skin biopsies, providing a biopsy-free solution for rapid diagnosis of malignant skin tumors. The technology uses deep-learning frameworks to transform images of intact skin into detailed, virtually stained images.
A new imaging method measures individual photons, greatly reducing interference and improving spatial resolution by three times. The technology could also reduce radiation exposure during x-ray imaging, making it ideal for medical applications.
The Stanford Computational Imaging Lab has developed a technique to reduce speckling distortion in holographic displays, while another paper proposes a method to realistically represent the physics of 3D scenes. The new system uses neural networks and camera-in-the-loop calibration for real-time adjustments.
Researchers at Rice University are creating a 3D-printed smart helmet with embedded sensors to protect soldiers' brains against kinetic or directed-energy effects. The program aims to modernize standard-issue military helmets by incorporating advances in materials, image processing, artificial intelligence, and energy storage.
Researchers have demonstrated a new technique for cross-sectional medical images without the need for tomography, enabling faster and more accurate imaging. The breakthrough is made possible by ultrafast photon detectors that can precisely determine the arrival times of photons, allowing for reconstruction-free positron emission imaging.
Researchers have trained a neural network to detect anomalies in medical images, adapting it to the nature of medical imaging and achieving better results. The new method uses weakly supervised training and can spot small-scale anomalies, accelerating the work of histopathologists and radiologists.
A team of researchers has created a virtual fitting room system using AI and a bespoke robotic mannequin. The system can digitize garments in two hours and synthesize photorealistic images, allowing users to try on clothing in real-time.
Researchers at Hebrew University developed a novel approach to mapping brain white matter fiber architecture using Nissl staining. The technique, called Nissl-ST, reveals the hidden patterns and organization of glial cells in white matter, opening new avenues for studying brain development, aging, and neurodegenerative diseases.
Researchers at the University of Waterloo developed AI technology that can identify players by their jersey numbers in hockey videos with high accuracy. The system uses multi-task learning and a large dataset of over 54,000 images from NHL games to recognize sweater numbers.
The Imageomics Institute, led by The Ohio State University, aims to use machine learning methodologies to extract biological traits from images of living organisms. Experts like Chuck Stewart will utilize computer vision and artificial intelligence to help infer phylogenetic traits from images.
Researchers at Helmholtz-Zentrum Berlin have achieved a new world record in materials research by using X-ray microscopy to create 1000 three-dimensional images per second. This allows for the non-destructive study of fast processes in materials, enabling researchers to gain insights into material properties and behavior.
Researchers at the University of Rochester have developed a time-domain single-pixel imaging technique that detects ultrafast light pulses with high accuracy and speed. The new method can capture 5 femtojoule pulses with temporal sampling sizes as low as 16 femtoseconds, outperforming existing methods.
Scientists have developed a technique to enhance digital sensor capabilities beyond current limits, enabling applications in consumer photography, medical imaging, and space exploration. The new approach uses 'modulo' sampling to process a wider range of information, unlocking high dynamic range for sensors.
A team of researchers from the Beckman Institute for Advanced Science and Technology has developed a fast, accurate, and cost-effective COVID-19 test. Using label-free microscopic imaging combined with artificial intelligence, they can detect and classify SARS-CoV-2 in under one minute.
Scientists at Anglia Ruskin University have created a new way to experience Yellowstone's geysers through classical music. By sonifying the physical characteristics of the geothermal landscape, they've turned vibrations into melodies, harmonies, and rhythms. The result is an evolving musical representation of the earth, water, and steam.
A team of researchers at Osaka University created a custom dataset to train an AI algorithm to digitally remove unwanted objects from building façade images. The algorithm achieved high accuracy in inpainting occluded regions with digital inpainting.