Jerry L. Prince

William B. Kouwenhoven Professor

Primary Appointment: Electrical and Computer Engineering

Research Interests

  • Image Processing
  • Computer vision with application to medical imaging

Jerry L. Prince has research interests in image processing and computer vision with primary application to medical imaging. He has studied and developed methods for imaging motion in the heart, tongue, and brain using magnetic resonance imaging. He has applied both statistical estimation and computer vision methods to the analysis of brain structure with applications in normal aging, Alzheimer’s disease, and multiple sclerosis. He is also co founder of Diagnosoft, Inc., a medical imaging software company. He received the BS degree from the University of Connecticut in 1979 and the S.M., E.E., and PhD degrees in 1982, 1986, and 1988, respectively, from the Massachusetts Institute of Technology, all in electrical engineering and computer science.

Awards and Honors

2017 Diversity Recognition Award

Enduring Impact Award, MICCAI Society

Fellow of the MICCAI Society

Secondary Appointments:  Applied Mathematics and Statistics; Computer Science; Radiology and Radiological Science; Biomedical Engineering, Johns Hopkins University School of Medicine


Journal Articles
  • Ye, C., Murano, E., Stone, M., Prince, J. (2015).  A Bayesian approach to distinguishing interdigitated tongue muscles from limited diffusion magnetic resonance imaging.  Computerized Medical Imaging and Graphics.  45.  63–74.
  • Jedynak, B., Liu, B., Lang, A., Gel, Y., Prince, J., Initiative, A. D. (2015).  A computational method for computing an Alzheimer’s Disease Progression Score; experiments and validation with the ADNI dataset.  Neurobiology of Aging.  36(Supplement 1).  S178–S184.
  • Woo, J., Lee, J., Murano, E. Z., Xing, F., Al-Talib, M., Stone, M., Prince, J. (2015).  A high-resolution atlas and statistical model of the vocal tract from structural MRI.  Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization.  3(1).  1–14.
  • Nyquist, P. A., Bilgel, M., Gottesman, R., Yanek, L. R., Moy, T. F., Becker, L. C., Cuzzocreo, J. L., Prince, J., Wasserman, B. A., Yousem, D. M., others. (2015).  Age differences in periventricular and deep white matter lesions.  Neurobiology of aging.  36(4).  1653–1658.
  • Bhargava, P., Lang, A., Al-Louzi, O., Carass, A., Prince, J., Calabresi, P. A., Saidha, S. (2015).  Applying an Open-Source Segmentation Algorithm to Different OCT Devices in Multiple Sclerosis Patients and Healthy Controls: Implications for Clinical Trials.  Multiple Sclerosis International.  2015.
  • Lang, A., Carass, A., Swingle, E. K., Al-Louzi, O., Bhargava, P., Saidha, S., Ying, H. S., Calabresi, P. A., Prince, J. (2015).  Automatic segmentation of microcystic macular edema in OCT.  Biomedical Optics Express.  6(1).  155–169.
  • Resnick, S. M., Bilgel, M., Moghekar, A., An, Y., Cai, Q., Wang, M., Thambisetty, M., Prince, J., Zhou, Y., Soldan, A., others. (2015).  Changes in A$β$ biomarkers and associations with APOE genotype in 2 longitudinal cohorts.  Neurobiology of Aging.  36(8).  2333–2339.
  • Bilgel, M., Carass, A., Resnick, S. M., Wong, D. F., Prince, J. (2015).  Deformation field correction for spatial normalization of PET images.  NeuroImage.  119.  152–163.
  • Nyquist, P. A., Yanek, L. R., Bilgel, M., Cuzzocreo, J. L., Becker, L. C., Chevalier-Davis, K., Yousem, D., Prince, J., Kral, B. G., Vaidya, D., others. (2015).  Effect of white matter lesions on manual dexterity in healthy middle-aged persons.  Neurology.  84(19).  1920–1926.
  • Jog, A., Carass, A., Roy, S., Pham, D. L., Prince, J. (2015).  MR Image Synthesis by Contrast Learning On Neighborhood Ensembles.  Medical Image Analysis.  24(1).  63–76.
  • Woo, J., Stone, M., Prince, J. (2015).  Multimodal Registration via Mutual Information Incorporating Geometric and Spatial Context.  Image Processing, IEEE Transactions on.  24(2).  757–769.
  • Carass, A., Lang, A., Hauser, M., Calabresi, P. A., Ying, H. S., Prince, J. (2015).  Multiple-object geometric deformable model for segmentation of macular OCT: errata.  Biomedical Optics Express.  6(4).  1351–1352.
  • Oh, J., Sotirchos, E. S., Saidha, S., Whetstone, A., Chen, M., Newsome, S. D., Zackowski, K., Balcer, L. J., Frohman, E., Prince, J., others. (2015).  Relationships between quantitative spinal cord MRI and retinal layers in multiple sclerosis.  Neurology.  84(7).  720–728.
  • Ye, C., Yang, Z., Ying, S. H., Prince, J. (2015).  Segmentation of the Cerebellar Peduncles Using a Random Forest Classifier and a Multi-object Geometric Deformable Model: Application to Spinocerebellar Ataxia Type 6.  Neuroinformatics.  1–15.
  • Ibragimov, B., Prince, J., Murano, E. Z., Woo, J., Stone, M., Likar, B., Pernus, F., Vrtovec, T. (2015).  Segmentation of tongue muscles from super-resolution magnetic resonance images.  Medical image analysis.  20(1).  198–207.
  • Saidha, S., Al-Louzi, O., Ratchford, J. N., Bhargava, P., Oh, J., Newsome, S. D., Prince, J., Pham, D., Roy, S., van Zijl, P., others. (2015).  Optical coherence tomography reflects brain atrophy in MS: A four year study.  Annals of Neurology.  78(5).  801–813.
  • Roy, S., He, Q., Sweeney, E., Carass, A., Reich, D. S., Prince, J., Pham, D. L. (2015).  Subject specific sparse dictionary learning for atlas based brain MRI segmentation.  IEEE Journal of Biomedical Health Informatics.  19(5).  1598–1609.
Book Chapters
  • Bai, Y., Han, X., Prince, J. (2015).  Geometric Deformable Models.  Handbook of Biomedical Imaging.  Springer US.  83–104.
Other Publications
Conference Proceedings
  • Lang, A., Carass, A., Bittner, A. K., Ying, H. S., Prince, J. (2015).  Automated segmentation of macular SD-OCT scans of retinitis pigmentosa patients shows regional patterns of foveal inner retinal thickening that correlate with visual function.  Investigative Ophthalmology & Visual Science.  56(7).  3811–3811.
  • Asoni, A., Ketcha, M., Kuo, N., Chen, L., Boctor, E., Coon, D., Prince, J. (2015).  B-Mode ultrasound pose recovery via surgical fiducial segmentation and tracking.  SPIE Medical Imaging.  94152C–94152C-8.
  • Woo, J., Xing, F., Lee, J., Stone, M., Prince, J. (2015).  Construction of An Unbiased Spatio-Temporal Atlas of the Tongue During Speech.  Information Processing in Medical Imaging.  723–732.
  • Sigurdsson, G. A., Yang, Z., Tran, T. D., Prince, J. (2015).  Interpretable exemplar-based shape classification using constrained sparse linear models.  SPIE Medical Imaging.  94130R–94130R-7.
  • Lang, A., Carass, A., Al-Louzi, O., Bhargava, P., Ying, H. S., Calabresi, P. A., Prince, J. (2015).  Longitudinal graph-based segmentation of macular OCT using fundus alignment.  SPIE Medical Imaging.  94130M–94130M-8.
  • Roy, S., Carass, A., Prince, J., Pham, D. L. (2015).  Longitudinal Patch-Based Segmentation of Multiple Sclerosis White Matter Lesions.  Machine Learning in Medical Imaging.  Springer International Publishing.  194–202.
  • Jog, A., Carass, A., Pham, D. L., Prince, J. (2015).  Multi-output decision trees for lesion segmentation in multiple sclerosis.  SPIE Medical Imaging.  94131C–94131C-6.
  • Ye, C., Glaister, J., Prince, J. (2015).  Probabilistic fiber tracking using a modified Lasso bootstrap method.  Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium on.  943–946.
  • Xing, F., Ye, C., Woo, J., Stone, M., Prince, J. (2015).  Relating speech production to tongue muscle compressions using tagged and high-resolution magnetic resonance imaging.  SPIE Medical Imaging.  94131L–94131L-6.
  • Swingle, E. K., Lang, A., Carass, A., Al-Louzi, O., Saidha, S., Prince, J., Calabresi, P. A. (2015).  Segmentation of microcystic macular edema in Cirrus OCT scans with an exploratory longitudinal study.  SPIE Medical Imaging.  94170P–94170P-9.
  • Bilgel, M., Jedynak, B., Wong, D. F., Resnick, S. M., Prince, J. (2015).  Temporal Trajectory and Progression Score Estimation from Voxelwise Longitudinal Imaging Measures: Application to Amyloid Imaging.  Information Processing in Medical Imaging.  424–436.
  • Jog, A., Carass, A., Pham, D. L., Prince, J. (2015).  Tree-Encoded Conditional Random Fields for Image Synthesis.  Information Processing in Medical Imaging.  733–745.
  • Chen, M., Jog, A., Carass, A., Prince, J. (2015).  Using image synthesis for multi-channel registration of different image modalities.  SPIE Medical Imaging.  94131Q–94131Q-7.
  • Ye, C., Zhuo, J., Gullapalli, R. P., Prince, J. (2015).  Estimation of fiber orientations using neighbohood information.  MICCAI 2015 Workshop on Computational Diffusion MRI.
  • Bilgel, M., An, Y., Zhou, Y., Wong, D. F., Prince, J., Resnick, S. M. (2015).  Age at onset of amyloid accumulation in relation to Apoe genotype.  Alzheimer’s Association International Conference (AAIC).
  • Bilgel, M., An, Y., Wong, D. F., Prince, J., Resnick, S. M. (2015).  APOE e4 allele is associated with an earlier onset of amyloid accumulation.  Human Amyloid Imaging Conference.
  • Bilgel, M., Resnick, S. M., Wong, D. F., Jedynak, B., Prince, J. (2015).  Temporal progression of cerebral amyloid deposition as measured by 11C-PiB PET imaging.  Human Amyloid Imaging Conference.
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