期刊
PEERJ COMPUTER SCIENCE
卷 -, 期 -, 页码 -出版社
PEERJ INC
DOI: 10.7717/peerj-cs.542
关键词
Morphology; Morphometrics; 2D shape analysis; Statistical analysis; Classical hypothesis testing; Spatial registration
类别
资金
- Japan Society for the Promotion of Science [17H02151]
- Grants-in-Aid for Scientific Research [17H02151] Funding Source: KAKEN
This computational framework provides an automated, landmark-free hypothesis testing of 2D contour shapes, yielding quick results with rich morphological detail and probability values. However, the framework is sensitive to algorithm parameters and sensitivity analysis is recommended for robust statistical conclusions.
This paper proposes a computational framework for automated, landmark-free hypothesis testing of 2D contour shapes (i.e., shape outlines), and implements one realization of that framework. The proposed framework consists of point set registration, point correspondence determination, and parametric full-shape hypothesis testing. The results are calculated quickly (<2 s), yield morphologically rich detail in an easy-to-understand visualization, and are complimented by parametrically (or nonparametrically) calculated probability values. These probability values represent the likelihood that, in the absence of a true shape effect, smooth, random Gaussian shape changes would yield an effect as large as the observed one. This proposed framework nevertheless possesses a number of limitations, including sensitivity to algorithm parameters. As a number of algorithms and algorithm parameters could be substituted at each stage in the proposed data processing chain, sensitivity analysis would be necessary for robust statistical conclusions. In this paper, the proposed technique is applied to nine public datasets using a two-sample design, and an ANCOVA design is then applied to a synthetic dataset to demonstrate how the proposed method generalizes to the family of classical hypothesis tests. Extension to the analysis of 3D shapes is discussed.
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