Our program continues to investigate means of presentation of medical images from a wide range of imaging modalities. The research, involving six projects and two cores, concerns techniques and clinical applications of 3D and 2D display for diagnosis and the planning of radiotherapy. Investigations into object definition and human vision develop models and methods supporting display technique development. In the 3D display area, 1) we will develop techniques for 3D exploration of medical image data and for visualization of complicated combinations of anatomy and planning objects, and 2) we will investigate 3D display applications in radiotherapy and pathology. In our 2D display work, 1) we will evaluate diagnostic uses of contrast improvements of mammographic, radiotherapy portal, and CT chest images, and b) we will develop and evaluate clinically usable diagnostic workstations in the field and in the laboratory, for both multimodality and mammographic display. Both our human vision project and our object definition project will focus on shape- and grouping-based perception and analysis in the complicated contexts found in medical images and will produce models of vision of shape. The object definition project will include application in both interactive 3D display and automatic, on-line analysis of radiotherapy portal images. Controlled evaluations will be conducted in all of the project areas. A statistics core will not only provide designs and data analyses for observer experiments but also development of improved statistics or analyses in observer studies with small samples of correlated observations, both where perceivability is rated and where continuous responses are give. The research results will allow smaller samples to provide guaranteed type I error rates and statistical power. A facilities core will support software and hardware facilities, administration, and ergonomic consultation.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Program Projects (P01)
Project #
5P01CA047982-08
Application #
2092805
Study Section
Special Emphasis Panel (SRC (S1))
Project Start
1988-07-03
Project End
1997-04-30
Budget Start
1995-05-01
Budget End
1997-04-30
Support Year
8
Fiscal Year
1995
Total Cost
Indirect Cost
Name
University of North Carolina Chapel Hill
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
078861598
City
Chapel Hill
State
NC
Country
United States
Zip Code
27599
Sadeghi, Neda; Prastawa, Marcel; Fletcher, P Thomas et al. (2012) STATISTICAL GROWTH MODELING OF LONGITUDINAL DT-MRI FOR REGIONAL CHARACTERIZATION OF EARLY BRAIN DEVELOPMENT. Proc IEEE Int Symp Biomed Imaging :1507-1510
El-Sayed, Mohamed; Steen, R Grant; Poe, Michele D et al. (2010) Brain volumes in psychotic youth with schizophrenia and mood disorders. J Psychiatry Neurosci 35:229-36
Chi, Yueh-Yun; Muller, Keith E (2010) Using scientifically and statistically sufficient statistics in comparing image segmentations. Stat Interface 3:91-101
Simpson, Sean L; Edwards, Lloyd J; Muller, Keith E et al. (2010) A linear exponent AR(1) family of correlation structures. Stat Med 29:1825-38
Clement-Spychala, Meagan E; Couper, David; Zhu, Hongtu et al. (2010) Approximating the Geisser-Greenhouse sphericity estimator and its applications to diffusion tensor imaging. Stat Interface 3:81-90
Edwards, Lloyd J; Muller, Keith E; Wolfinger, Russell D et al. (2008) An R2 statistic for fixed effects in the linear mixed model. Stat Med 27:6137-57
Hemminger, Bradley M; Bauers, Anne; Yang, Jian (2008) Comparison of navigation techniques for large digital images. J Digit Imaging 21 Suppl 1:S13-38
Crouch, Jessica R; Pizer, Stephen M; Chaney, Edward L et al. (2007) Automated finite-element analysis for deformable registration of prostate images. IEEE Trans Med Imaging 26:1379-90
Gurka, Matthew J; Coffey, Christopher S; Muller, Keith E (2007) Internal pilots for a class of linear mixed models with Gaussian and compound symmetric data. Stat Med 26:4083-99
Muller, Keith E; Edwards, Lloyd J; Simpson, Sean L et al. (2007) Statistical tests with accurate size and power for balanced linear mixed models. Stat Med 26:3639-60

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