Online Statistical Inference for Large-Scale Binary Images
We present a unified online statistical framework for quantifying a collection of binary images. Since medical image segmentation is often done semi-automatically, the resulting binary images may be available in a sequential manner. Further, modern medical imaging datasets are too large to fit into a computer’s memory. Thus, there is a need to develop an iterative analysis framework where the final statistical maps are updated sequentially each time a new image is added to the analysis. We propose a new algorithm for online statistical inference and apply to characterize mandible growth during the first two decades of life.
This work was supported by NIH Research Grants R01 DC6282, P-30 HD03352, UL1TR000427 and R01 EB022856.
- 7.Finch, T.: Incremental calculation of weighted mean and variance. University of Cambridge, 4:11–4:15 (2009)Google Scholar
- 8.Jaakkola, T., Jordan, M.: A variational approach to bayesian logistic regression models and their extensions. In: Sixth International Workshop on Artificial Intelligence and Statistics, vol. 82 (1997)Google Scholar
- 9.Karp, R.M.: On-line algorithms versus off-line algorithms: how much is it worth to know the future? In: IFIP Congress, vol. 12, pp. 416–429 (1992)Google Scholar