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.
CITATION STYLE
Chung, M. K., Chuang, Y. J., & Vorperian, H. K. (2017). Online statistical inference for large-scale binary images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10434 LNCS, pp. 729–736). Springer Verlag. https://doi.org/10.1007/978-3-319-66185-8_82
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