Associate Professor in Radiology
My research has centered on multivariate image based phenotyping, with a focus on neurological conditions like Alzheimer’s disease. I work on imaging and analysis techniques to provide a comprehensive characterization of the brain. MRI is particularly suitable for brain imaging, and diffusion tensor imaging is an important tool for studying white matter, and the connectivity amongst gray matter regions. Using such techniques, we have developed high resolution, multivariate population atlases for animal models.
I am interested in image segmentation, morphometry and shape analysis, as well as in integrating information from MRI with genetics, and behavior. Our approaches target: 1) phenotyping the neuroanatomy using imaging; 2) uncovering the link between structural and functional changes, the genetic bases, and environmental factors. I am interested in generating methods and tools for comprehensive phenotyping.
The unique setting of the Center for In Vivo Microscopy (CIVM) provides most imaging modalities for small animals: several MRI systems, micro-CT, SPECT, and multi-photon microscopy, which allow us to integrate imaging data from multiple modalities, and across scales. We use high-performance cluster computing to accelerate our image analysis. We use compressed sensing image reconstruction, and process large image arrays using deformable registration, perform segmentation based on multiple image contrasts including diffusion tensor imaging, as well as voxel based analysis, and graph analysis for connectomics. These strategies will help increase the rate at which we grow our current understanding of gray and white matter changes in neurological and psychiatric conditions.
My enthusiasm comes from the possibility to extend from single to integrative multivariate and network based analyses to obtain a comprehensive picture of normal development and aging, stages of disease, and the effects of treatments. I am looking forward to continue working on multivariate image analysis and predictive modeling approaches to help better understand early biomarkers for human disease indirectly through mouse models, as well as directly.
Appointments and Affiliations
- Associate Professor in Radiology
- Assistant Professor of Biomedical Engineering
- Member of the Center for Brain Imaging and Analysis
- Office Location: Center for in Vivo Microscopy, Room 139 Bryan Research Building, Durham, NC 27710
- Office Phone: (919) 684-7654
- Email Address: firstname.lastname@example.org
- Ph.D. University of Patras (Greece), 2003
Brain Imaging, MRI, Connectivity, Multivariate Biomarkers, Image Analysis, Neurological Conditions, Alzheimer's Disease
- ECE 899: Special Readings in Electrical Engineering
- Wu, T; Bae, MH; Zhang, M; Pan, R; Badea, A, A prior feature SVM-MRF based method for mouse brain segmentation., Neuroimage, vol 59 no. 3 (2012), pp. 2298-2306 [10.1016/j.neuroimage.2011.09.053] [abs].
- Poot, M; Badea, A; Williams, RW; Kas, MJ, Identifying human disease genes through cross-species gene mapping of evolutionary conserved processes., Plos One, vol 6 no. 5 (2011) [10.1371/journal.pone.0018612] [abs].
- Bowden, DM; Johnson, GA; Zaborsky, L; Green, WDK; Moore, E; Badea, A; Dubach, MF; Bookstein, FL, A symmetrical Waxholm canonical mouse brain for NeuroMaps., J Neurosci Methods, vol 195 no. 2 (2011), pp. 170-175 [10.1016/j.jneumeth.2010.11.028] [abs].
- Johnson, GA; Badea, A; Jiang, Y, Quantitative neuromorphometry using magnetic resonance histology., Toxicol Pathol, vol 39 no. 1 (2011), pp. 85-91 [10.1177/0192623310389622] [abs].
- Johnson, GA; Badea, A; Brandenburg, J; Cofer, G; Fubara, B; Liu, S; Nissanov, J, Waxholm space: an image-based reference for coordinating mouse brain research., Neuroimage, vol 53 no. 2 (2010), pp. 365-372 [10.1016/j.neuroimage.2010.06.067] [abs].
- Badea, A; Johnson, GA; Jankowsky, JL, Remote sites of structural atrophy predict later amyloid formation in a mouse model of Alzheimer's disease., Neuroimage, vol 50 no. 2 (2010), pp. 416-427 [10.1016/j.neuroimage.2009.12.070] [abs].
- Badea, A; Johnson, GA; Williams, RW, Genetic dissection of the mouse CNS using magnetic resonance microscopy., Curr Opin Neurol, vol 22 no. 4 (2009), pp. 379-386 [10.1097/WCO.0b013e32832d9b86] [abs].
- Bae, MH; Pan, R; Wu, T; Badea, A, Automated segmentation of mouse brain images using extended MRF., Neuroimage, vol 46 no. 3 (2009), pp. 717-725 [10.1016/j.neuroimage.2009.02.012] [abs].
- Badea, A; Johnson, GA; Williams, RW, Genetic dissection of the mouse brain using high-field magnetic resonance microscopy., Neuroimage, vol 45 no. 4 (2009), pp. 1067-1079 [10.1016/j.neuroimage.2009.01.021] [abs].
- Sharief, AA; Badea, A; Dale, AM; Johnson, GA, Automated segmentation of the actively stained mouse brain using multi-spectral MR microscopy., Neuroimage, vol 39 no. 1 (2008), pp. 136-145 [10.1016/j.neuroimage.2007.08.028] [abs].
- Driehuys, B; Nouls, J; Badea, A; Bucholz, E; Ghaghada, K; Petiet, A; Hedlund, LW, Small animal imaging with magnetic resonance microscopy., Ilar Journal, vol 49 no. 1 (2008), pp. 35-53 [10.1093/ilar.49.1.35] [abs].
- Badea, A; Ali-Sharief, AA; Johnson, GA, Morphometric analysis of the C57BL/6J mouse brain., Neuroimage, vol 37 no. 3 (2007), pp. 683-693 [10.1016/j.neuroimage.2007.05.046] [abs].
- Johnson, GA; Ali-Sharief, A; Badea, A; Brandenburg, J; Cofer, G; Fubara, B; Gewalt, S; Hedlund, LW; Upchurch, L, High-throughput morphologic phenotyping of the mouse brain with magnetic resonance histology., Neuroimage, vol 37 no. 1 (2007), pp. 82-89 [10.1016/j.neuroimage.2007.05.013] [abs].
- Badea, A; Nicholls, PJ; Johnson, GA; Wetsel, WC, Neuroanatomical phenotypes in the reeler mouse., Neuroimage, vol 34 no. 4 (2007), pp. 1363-1374 [10.1016/j.neuroimage.2006.09.053] [abs].
- Ali, AA; Dale, AM; Badea, A; Johnson, GA, Automated segmentation of neuroanatomical structures in multispectral MR microscopy of the mouse brain., Neuroimage, vol 27 no. 2 (2005), pp. 425-435 [10.1016/j.neuroimage.2005.04.017] [abs].
- Badea, A; Kostopoulos, GK; Ioannides, AA, Surface visualization of electromagnetic brain activity., Journal of Neuroscience Methods, vol 127 no. 2 (2003), pp. 137-147 [10.1016/s0165-0270(03)00100-6] [abs].