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Automated Histological Image Processing tool for Identifying and Quantifying Tissue Calcification

Researchers at UCI have developed a method of identifying, quantifying, and visualizing tissue with calcification. The image processing tool can automatically characterize calcium deposits in CT images histological tissue, especially when it has accumulated in unusual places in the body.

System For Determining Trademark Similarity

Many areas of intellectual property law involve subjective judgments regarding confusion or similarity. For example, in trademark or trade dress lawsuits a key factor considered by the court is the degree of visual similarity between the trademark or product designs under consideration. Such similarity judgments are nontrivial, and may be complicated by cognitive factors such as categorization, memory, and reasoning that vary substantially across individuals. Currently, three forms of evidence are widely accepted: visual comparison by litigants, expert witness testimonies, and consumer surveys. All three rely on subjective reports of human responders, whether litigants, expert witnesses, or consumer panels. Consequently, all three forms of evidence potentially share the criticism that they are subject to overt (e.g. conflict of interest) or covert (e.g. inaccuracy of self-report) biases.To address this situation, researchers at UC Berkeley developed a technology that directly measures the mental state of consumers when they attend to visual images of consumer products, without the need for self-report measures such as questionnaires or interviews. In so doing, this approach reduces the potential for biased reporting.  

Wireless and Programmable Recording and Stimulation of Deep Brain Activity in Freely Moving Humans Immersed in Virtual, Augmented or Real-World Environments

UCLA researchers in the Department of Psychiatry and Biobehavioral Sciences have a designed a lightweight, highly mobile deep brain activity measuring platform that elucidates neural mechanisms for neuropsychiatric disorders.

Generating Visual Analytics and Player Statistics for Soccer

Prof. Bhanu and his colleagues from the University of California, Riverside have developed a system to automate the process of player talent identification by performing visual analytics and generating statistics at the match, team and player level for soccer from a video using computer vision and machine learning techniques. This work uses a database of 49,952 images which are annotated into two classes namely: players with the ball and players without the ball. The system can identify which players are controlling the ball. Compared to other state-of-the-art approaches, this technology has demonstrated an accuracy of 86.59% on identifying players controlling the ball and an accuracy of 84.73% in generating the match analytics and player statistics. Figure 1: Visualization of features learned by the system Figure 2: Visualization of gray scale features learned by the system  

Design Of Task-Specific Optical Systems Using Broadband Diffractive Neural Networks

UCLA researchers in the Department of Electrical and Computer Engineering have developed a diffractive neural network that can process an all-optical, 3D printed neural network for deep learning applications.

A Fully‐automated Deep Learning System (software code) for the Detection, Prognosis, and Visualization of Pulmonary Disease.

The majority of state‐of‐the‐art lung segmentation algorithms in the literature do not simultaneously segment lungs, lung lobes, and airway in a single algorithm. Additionally, automated algorithms typically perform the segmentation task on a series of 2D slices, which can reduce segmentation accuracy of anatomical structures (i.e. lung lobes) that may require contextual information across all three spatial dimensions. Many existing algorithms also have not been validated on chest CTs across a wide variety of conditions to evaluate algorithm generalizability. Currently, quantification of respiratory measurements requires a radiologist, trained analyst, or technician to recognize, identify, and manually annotate anatomical landmarks such as the lung lobes or airway in the chest. A fully‐automated deep learning system may eliminate the need for manual analysis, thereby improving efficiency and expanding applicability to a large number of CTs.

Software-Automated Medical Imaging Software for Standardizing the Diagnosis of Sarcopenia

Sarcopenia  is defined as an age associated decline in or loss of lean skeletal muscle mass. The pathophysiology can be multifactorial and the change in body composition may be difficult to detect due to obesity, changes in fat mass, or edema. Changes in weight, limb or waist circumference are not reliable indicators of muscle mass changes. Sarcopenia may also cause reduced strength, functional decline and increased risk of falling. Sarcopenia is otherwise asymptomatic and is often unrecognized.  

Software - Unified algorithm for data cleaning, source separation, and imaging of electroencephalographic signals: Recursive Sparse Bayesian Learning (RSBL)

Electroencephalographic source imaging (a.k.a. magnetic/electric or M/EEG source imaging, ESI, or brain electrical tomography) usually depends upon sophisticated signal processing algorithms for data cleaning, source separation and imaging. Typically, these problems are addressed separately using a variety of heuristics, making it difficult to systematize a methodology for extracting robust brain source images on a wide range of applications.

Lambda-Reservoir Computing

UCLA researchers in the Department of Electrical and Computer Engineering have developed a Spectral Reservoir Computer that processes data using nonlinear optical interactions.

AI Enabled UAV Route-Planning Algorithm with Applications to Search and Surveillance

Portable UAVs such as quad-copters have made huge inroads in the last several years in various fields of aerial photography and surveillance. Drones can efficiently and cheaply hover over/follow a target of interest and capture unique perspectives of wildlife, real-estate, sporting events and operational environments such as law enforcement or military. More challenging however is the application of UAVs for large area search and surveillance. In these scenarios, a search pattern must be established which can cover many square miles and is far too expansive for a UAVs typical battery to sustain. To make UAVs more broadly effective in large area search and target identification, new path planning algorithms are needed to efficiently eliminate areas of low probability while focusing on search areas most likely to contain the subject of interest. Likewise, improved image classifiers are needed to aid in separating targets of interest from background terrain, thus expediting the search within given battery limitations

Extended Depth-Of-Field In Holographic Image Reconstruction Using Deep Learning-Based Auto-Focusing And Phase-Recovery

UCLA researchers in the Department of Electrical Engineering have developed a novel deep learning-based algorithm that digitally reconstructs images from holography over an extended depth of field.

Real-time 3D Image Processing Platform for Visualizing Blood Flow Dynamics

Researchers at UCI have developed an image processing platform capable of visualizing 3D blood flow dynamics of the heart in real-time. This technology aims to be a promising tool for looking at areas of the heart that were previously difficult to image and to better understand the dynamics in cardiac dysfunctions.

A New Human-Monitor Interface For Interpreting Clinical Images

UCLA researchers in the Department of Radiological Sciences have invented a novel interactive tool that can rapidly focus and zoom on a large number of images using eye tracking technology.

A Method For Accurate Parametric Mapping Based On Characterization Of A Reference Tissue Or Region

UCLA researchers in the Department of Radiology have developed a novel method that addresses a common issue of MRI imaging misinterpretation due to the high field effects of B1+ inhomogeneity.

Dicom/Pacs Compression Techniques

Researchers led by Xiao Hu from the Department of Surgery at UCLA have created a novel and convenient way to compress and query medical images from a PACS system.

A New Format For Representing And Encoding Images

Researchers in the Statistics and Computer Science Departments at UCLA have developed a method for image compression that is 5x more efficient than JPEG image coding.

Deep-Learning-Based Computerized Prostate Cancer Classification Using A Hierarchical Classification Framework

UCLA researchers in the Department of Radiological Sciences have developed a deep-learning-based computerized algorithm for classification of prostate cancer using multi-parametric-MRI images.

Fast Implementation Of Equally-Sloped Tomography

Dr. Miao and colleagues at UCLA have developed a novel algorithm that quickly processes high quality image reconstruction of data acquired through Equally-Sloped Tomography.

Collimator/Image Reconstruction Molecular Breast Imaging

MBI and BSGI utilize γ-cameras in a mammographic configuration to provide functional images of the breast. Several studies have confirmed that MBI has a high sensitivity for the detection of small breast lesions, independent of tumor type. A large clinical trial compared MBI with screening mammography in over 1000 women with mammographically dense breast tissue and increased risk of breast cancer and showed that MBI detected two to three times more cancers than mammography. Despite these favorable results, BSGI and MBI have not been widely accepted for breast cancer screening due to greater effective radiation dose compared with mammography. Another disadvantage of MBI is long imaging time, causing discomfort to the patient. Furthermore, while digital breast tomosynthesis (DBT) produces 3D images, resulting in improved cancer detection over mammography, current clinical MBI and BSGI systems produce only 2D images. These disadvantages are due to the use of parallel hole collimator (PHC) with MBI and BSGI, which is inefficient, allowing only gamma rays traveling perpendicular to the detector to be recorded. Furthermore, PHA cannot produce a 3D image with a stationary detector and results in a loss of image resolution with increasing distance between the tumor and the gamma detector.

3D Population Maps for Noninvasively Identifying Phenotypes and Pathologies in Individual Patients

UCLA researchers in the Department of Radiological Sciences have developed a novel computation system that uses large imaging datasets to aid in clinical diagnosis and prognosis.

Integrative Leakage Correction For Contrast Agent Extravasation In Dynamic Susceptibility Contrast (DSC) - MRI

UCLA researchers in the Department of Radiological Sciences have developed a new technique for more accurately estimating relative cerebral blood volume (rCBV) from dynamic susceptibility contrast (DSC) perfusion MRI by improved modeling and correction of contrast agent leakage.

Multi-Echo Spin-, Asymmetric Spin-, And Gradient Echo Echoplanar Imaging (Message-EPI) MRI

UCLA researchers in the Department of Radiological Sciences have developed a new MRI pulse sequence optimized for brain imaging.

Cloud based platform for display and analysis of image time series

Current microscopy systems commonly used in biomedical research labs and companies generate large amounts of large data, known as image stacks. There is currently no easy, streamlined way to store, organize and analyze these datasets on a cloud. Researchers at UCI have developed a software consisting of a cloud-based data management and analysis platform that make visualization and analysis of large image stacks simpler and faster.

Anisotropic Elastoplasticity For Codimensional Frictional Contact

UCLA researchers in the Department of Mathematics and Department of Computer Science have developed a novel hybrid Lagrangian/Eulerian approach to simulate frictional contact in thin codimensional elastic objects, such as cloth, hair, and knit. It allows a more smooth and vivid animation of those objects with faster speed and higher robustness.

Reducing Computational Complexity of Training Algorithms for Artificial Neural Networks

Researchers at UCLA have developed a novel mathematical theorem to revolutionize the training of large-scale artificial neural networks (ANN).

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