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Statistical Hypothesis Testing for Postreconstructed and Postregistered Medical Images.
SIAM Journal on Imaging Sciences ( IF 2.1 ) Pub Date : 2009-10-01 , DOI: 10.1137/080722199
Eugene Demidenko 1
Affiliation  

Postreconstructed and postregistered medical images are typically treated as the raw data, implicitly assuming that those operations are error free. We question this assumption and explore how the precision of reconstruction and affine registration can be assessed by the image covariance matrix and confidence interval, called the confidence eigenimage, using a statistical model-based approach. Various hypotheses may be tested after image reconstruction and registration using classical statistical hypothesis testing vehicles: Is there a statistically significant difference between images? Does the intensity at a specific location or area of interest belong to the "normal" range? Is there a tumor? Does the image require rigid registration? We illustrate statistical hypothesis testing with three examples: breast computed tomography, breast near infrared linear reconstruction, and brain magnetic resonance imaging.

中文翻译:

后重建和后注册医学图像的统计假设检验。

后重建和后配准的医学图像通常被视为原始数据,隐含地假设这些操作没有错误。我们质疑这一假设并探索如何使用基于统计模型的方法通过图像协方差矩阵和置信区间(称为置信特征图像)来评估重建和仿射配准的精度。在使用经典统计假设测试工具进行图像重建和配准后,可以测试各种假设:图像之间是否存在统计上的显着差异?特定位置或感兴趣区域的强度是否属于“正常”范围?有肿瘤吗?图像是否需要严格配准?我们用三个例子来说明统计假设检验:乳房计算机断层扫描、
更新日期:2019-11-01
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