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Mendeley readers
Chapter title |
Online Statistical Inference for Large-Scale Binary Images
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Chapter number | 82 |
Book title |
Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017
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Published in |
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, September 2017
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DOI | 10.1007/978-3-319-66185-8_82 |
Pubmed ID | |
Book ISBNs |
978-3-31-966184-1, 978-3-31-966185-8
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Authors |
Moo K. Chung, Ying Ji Chuang, Houri K. Vorperian |
Abstract |
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. |
Mendeley readers
The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 9 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Bachelor | 2 | 22% |
Student > Doctoral Student | 1 | 11% |
Student > Ph. D. Student | 1 | 11% |
Student > Master | 1 | 11% |
Student > Postgraduate | 1 | 11% |
Other | 0 | 0% |
Unknown | 3 | 33% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 3 | 33% |
Mathematics | 1 | 11% |
Psychology | 1 | 11% |
Engineering | 1 | 11% |
Unknown | 3 | 33% |