Johns Hopkins Biomedical Engineering primary faculty
Michael I. Miller, PhD
Herschel and Ruth Seder Professor and
University Gilman Scholar
Director, Center for Imaging Science
Co-Director, Kavli Neuroscience Discovery Institute
Office: Clark Hall 301
Lab: Center for Imaging Science
B.S.E.E. State University of NY at Stony Brook, 1976
M.S.E.E., The Johns Hopkins University, 1978
Ph.D., B.M.E., The Johns Hopkins University, 1983
Medical image understanding is where speech and language modeling was in 1980. Back then there were few speech, language and text understanding systems. Most of the greatest work had been focusing on the digital and acoustic stream; little progress had been made on the linguistics and structural representation of the language itself. This has all changed, with the entire focus moving away from the acoustic models to the higher level representations of information. Analogously, today in imaging there is a plethora of magnificent imaging devices of all kinds, at all scales, at all prices. However, there is little in the way of image understanding systems, systems which bring value not from the measured picture but from knowledge bases in the world. Think about it, in the context of Medical image reconstruction, could it really be that there is more information from a single MRI scan than in all of the books that Neuroscientists and Radiologists have catalogued since the Renaissance era of da Vinci and Michelangelo? Why then are there no examples of medical imaging devices which inject such information into their modality.
The difficulty has been that biological shape is exquisitely variable, with few methods for computing or measuring and computing the closeness of the geometric representation of normal and abnormal anatomical and biological structures. Without such “parsing algorithms” there are no structured ways to build models of real-world knowledge, or for inserting book knowledge into the medical image understanding algorithms. This is all changing now with the emergence of Computational Anatomy in the field of Medical Imaging. Computational Anatomy, like Computational Linguistics, allows for the semantic representation of brains into their parts and connectivities.
Biomedical engineering students Manisha Aggarwal, Kwame Kutten, Daniel Tward, Yajing Zhang, are currently engaged in the characterization of the human populations, studying the functional and structural characteristics of the human brain. Disease populations are being studied associated to Huntington’s disease, dementia, bipolar disorder, schizophrenia and epilepsy. We are currently hard at work building brain clouds for delivering image understanding algorithms associated to neuropsychiatric illness.
Michael I. Miller, Andreia V. Faria, Kenichi Oishi, and Susumu Mori, "High-throughput neuro-imaging informatics," Front Neuroinform. 2013; 7: 31.
Susumu Mori, Kenichi Oishi, Andreia V. Faria, Michael I. Miller, "Atlas-Based Neuroinformatics via MRI: Harnessing Information from Past Clinical Cases and Quantitative Image Analysis for Patient Care ," Annu Rev Biomed Eng. Author manuscript; available in PMC 2013 July 23.
D. J. Tward, J. Ma, M. I. Miller, L. Younes, "Robust Diffeomorphic mapping via Geodesically Controlled Active Shapes," International Journal of Biomedical Imaging, N pages (2013).
Y. Zhang J. Zhang, et al., Atlas Guided Tract Reconstruction for Automated Examination of the White Matter Anatomy, Neuroimage, 2010, vol. 52, no. 4, 1289-1301.M.I. Miller, Computational Anatomy: shape, growth and atophy comparison via diffeomorphisms, Neuroimage, 2004, vol. 23, Suppl 1: S19-33.
U. Grenander and M. I. Miller, "Computational Anatomy: An Emerging Discipline," Quart. App. Math., 1998, vol. 56, pp. 617-694.