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Practical Morphometrics Analysis (3D Model)

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  • 2,214 名學生
  • 更新於 2/2020
4.2
(23 個評分)
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課程資料

報名日期
全年招生
課程級別
學習模式
修業期
3 小時 47 分鐘
教學語言
英語
授課導師
Olalekan Agbolade
評分
4.2
(23 個評分)
1次瀏覽

課程簡介

Practical Morphometrics Analysis (3D Model)

A step-by-step approach to morphometrics based on three-dimensional images

Morphometrics has experienced a major revolution through the invention of coordinate-based methods, the discovery of the statistical theory of shape, and the computational realization of deformation grids. The ubiquitous application of fast personal computers and modern analytical tools have ushered in a new era of data analysis, permitting the exploration and visualization of large high-dimensional data sets along with exact statistical tests based on resampling procedures. This new morphometric approach has been termed geometric morphometrics as it preserves the geometry of the landmark configurations throughout the analysis and thus permits to represent statistical results as actual shapes or forms. Therefore, these lectures aim at teaching practically, the concept of statistical shape analysis from 3D images. To encourage learning by exploration; images, annotations and data reports from the hand study are made available for download.

課程章節

  • 4 個章節
  • 22 堂課
  • 第 1 章 Introduction
  • 第 2 章 Landmark and Acquisition in 3D
  • 第 3 章 Visualizing 3D Landmarks
  • 第 4 章 Statistical Analysis on 3D Landmark Data

課程內容

  • Introduction to morphometrics covering definitions, traditional morphometrics and geometric morphometrics, Landmarks and acqusition tools covering landmark types (anatomical or biological, mathematical, pseudo-landmarks), landmark homology, landmark acquisition tools and how to use, and error assessment with Procrustes ANOVA, Landmarks Visualization covering General Procrustes Analysis (GPA), visualization tools, scatter plots of landmark coordinates, Principal Component Analysis (PCA), Statistical methods and Analysis covering ANOVA, MANOVA, ANOSIM/PERMANOVA, regression & allometry, discriminant analysis and canonical variates analysis, clustering, EDMA


評價

  • S
    Srikant Natarajan
    5.0

    Excellent compilation. We had started a similar research and had lot of trouble understanding and assimilating. Dr. Olelakan has got the information spoton as required.

  • R
    Roy Minden Farman
    5.0

    Very clear and easy to follow along. Amazing job!

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