Image Analysis at Scale for Finding the Links Between Structure and Biology
Image data is growing at a rapid rate, whether from the continuous uploads on video portals, photo-sharing platforms, new satellites, or even medical data. The volumes have grown from tens of gigabytes to exabytes per year in less than a decade. Deeply embedded inside these datasets is detailed information on fashion trends, natural disasters, agricultural output, or looming health risks. The large majority of statistical analysis and data science is performed on numbers either as individuals or sequences. Images, however, do not neatly fit into the standard paradigms and have resulted in “graveyards” of large stagnant image storage systems completely independent of the other standard information collected. In this chapter, we will introduce the basic concepts of quantitative image analysis and show how such work can be used in the biomedical context to link hereditary information (genomic sequences) to the health or quality of bone. Since inheritance studies are much easier to perform if you are able to control breeding, the studies are performed in mice where in-breeding and cross-breeding are possible. Additionally, mice and humans share a large number of genetic and biomechanical similarities, so many of the results are transferable (Ackert-Bicknell et al. Mouse BMD quantitative trait loci show improved concordance with human genome-wide association loci when recalculated on a new, common mouse genetic map. Journal of Bone and Mineral Research 25(8):1808–1820, 2010).
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- Ackert-Bicknell, C. L., Karasik, D., Li, Q., Smith, R. V., Hsu, Y.-H., Churchill, G. A., et al. (2010). Mouse BMD quantitative trait loci show improved concordance with human genome-wide association loci when recalculated on a new, common mouse genetic map. Journal of Bone and Mineral Research: the Official Journal of the American Society for Bone and Mineral Research, 25(8), 1808–1820. https://doi.org/10.1002/jbmr.72.CrossRefGoogle Scholar
- Almeer, M. H. (2012). Cloud hadoop map reduce for remote sensing image analysis. Journal of Emerging Trends in Computing and Information Sciences, 3(4), 637–644.Google Scholar
- Blomfeldt, R., Törnkvist, H., Ponzer, S., Söderqvist, A., & Tidermark, J. (2005). Internal fixation versus hemiarthroplasty for displaced fractures of the femoral neck in elderly patients with severe cognitive impairment. Journal of Bone and Joint Surgery (British), 87(4), 523–529. https://doi.org/10.1302/0301-620X.87B4.15764.CrossRefGoogle Scholar
- Jansen, R. C., & Stam, P. (1994). High resolution of quantitative traits into multiple loci via interval mapping. Genetics, 136(4), 1447–1455.Google Scholar
- Mader, K., & Stampanoni, M. (2016, January). Moving image analysis to the cloud: A case study with a genome-scale tomographic study. In AIP Conference Proceedings (Vol. 1696, No. 1, p. 020045). AIP Publishing.Google Scholar
- Schwarz, H., & Exner, H. E. (1983). The characterization of the arrangement of feature centroids in planes and volumes. Journal of Microscopy, 129(2), 155–169. https://doi.org/10.1111/j.1365-2818.1983.tb04170.x.CrossRefGoogle Scholar
- Silver, L. M. (1995). Mouse genetics: Concepts and applications. Oxford: Oxford University Press.Google Scholar