Online Statistical Inference for Large-Scale Binary Images

  • Moo K. ChungEmail author
  • Ying Ji Chuang
  • Houri K. Vorperian
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10434)


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.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Moo K. Chung
    • 1
    • 2
    Email author
  • Ying Ji Chuang
    • 2
  • Houri K. Vorperian
    • 2
  1. 1.Department of Biostatistics and Medical InformaticsUniversity of WisconsinMadisonUSA
  2. 2.Vocal Tract Development Laboratory, Waisman CenterUniversity of WisconsinMadisonUSA

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