National Institute of Biomedical Imaging and Bioengineering | A part of the NIH focused on creating biomedical technologies to improve health.

Image Processing, Visual Perception and Display

Behrouz Shabestari

Ph.D.

Director - National Technologies Centers Program

301-451-6771

shabestb@mail.nih.gov

Qi Duan

Ph.D.

Program Director

301-451-4780

qi.duan@nih.gov

This program supports the design and development of algorithms for post-acquisition image processing and analysis, the development of theoretical models and analysis tools to evaluate and improve the perception of medical images, and the development of visualization tools for improved detection.

Emphasis

The emphasis is on using image data to achieve better health outcomes and smarter health care. Examples of technology development areas in this program include but are not limited to models, algorithms, software, methodologies, and other tools that will: facilitate medical imaging research; support clinical detection, diagnosis and therapy; and improve patient healthcare.

Program priorities and areas of interest:

Image segmentation, image registration, atlas generation, image fusion, morphometry measurement, and the determination of function and structure from medical images
Diagnostic-performance evaluation, computer-aided diagnosis, statistical models for evaluation of observer performance, and assessment of observer variability
Quantitative imaging and image-based biomarkers
Image-driven computer-aided diagnosis and decision support systems
Virtual reality technologies
Dose estimation and reduction software

Additional support

This program also supports:

Early-stage validation of tools for image processing, visual perception and display
Tools to assess image quality and observer performance
Tools and software that enable large-scale, longitudinal and/or multi-site imaging studies and clinical trials
Medical imaging mobile apps for early detection

Funded Projects

Grant Number	Project Title	Principal Investigator	Institution
5-R01-EB022744-04	Computational Tools for Modeling Human and Mouse Connectome with Multi-Shell Diffusion Imaging	Yonggang Shi	University of Southern California
5-R01-EB020062-04	Neurodegenerative and Neurodevelopmental Subcortical Shape Diffeomorphometry	Michael Miller	Johns Hopkins University
5-T32-EB016640-07	Medical Imaging Informatics Training Grant	Alex Bui	University of California Los Angeles
1-R03-EB028070-01	Dose Reduction in Pediatric/Adolescent Brain PET Imaging Through Artificial Neural Networks	Jing Tang	Oakland University
5-R01-EB017255-05	Adaptive Large-Scale Framework for Automatic Biomedical Image Segmentation	Paul Yushkevich	University of Pennsylvania
5-R01-EB017230-04	Controlling Quality and Capturing Uncertainty in Advanced Diffusion Weighted MRI	Bennett Landman	Vanderbilt University
1-K01-EB027726-01A1	Multiparametric multimodal imaging for breast cancer neoadjuvant chemotherapy monitoring	Bin Deng	Massachusetts General Hospital
1-R13-EB029304-01	IEEE International Symposium on Biomedical Imaging (ISBI) 2020	Mona Garvin	University of Iowa
5-R01-EB026859-08	Spatial inference methods for image analysis	Armin Schwartzman	University of California, San Diego
5-R01-EB014955-06	Accelerating Community-Driven Medical Innovation with VTK	Ken Martin	Kitware, Inc.

Related Multimedia

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QPath: A Flow-Through High-Throughput Quantitative Histology Platform In the category of Diagnostic Devices the winning project was Q-Path, submitted by Armin Arshi, David Kuo, Robert Lee, Elizabeth Ng, and Andrew Tan from the University of California Los Angeles. The project addressed the most common form of bladder cancer, transitional cell carcinoma (TCC), which is the fourth most common and ninth most deadly form of cancer in men. The team developed a high-throughput, flow-through microfluidic platform combined with automated image analysis software, which allows for systematic screening of patients' urine samples in order to noninvasively diagnose TCC. The system provides the pathologist with a quantitative analysis of the sample and an index to differentiate between healthy, low-grade malignancy, and high-grade malignancy. The device has the potential to be applied to a broader range of bodily fluid samples, including blood and pleural fluids; hence it could play a key role in the early diagnosis of various types of cancers. For more information on DEBUT visit: http://www.nibib.nih.gov/training-careers/undergraduate-graduate/design…