Introduction to Multivariate Image Analysis

Course Description

Intro to Multivariate Image Analysis is designed to give the student practical experience. Before the course, students will be sent a precourse reading assignment covering some of the basic background and principles of MIA. The course will start with a brief review of principal components analysis (PCA) and partial least squares (PLS) regression and how they are used in image analysis. Additional topics to be covered included multivariate image regression, and preprocessing to capture textural information. Methods to mitigate the effects of background interference, e.g. clutter, will also be discussed. The course includes hands-on computer time for participants to work example problems using PLS_Toolbox, MIA_Toolbox, and MATLAB.

Prerequisites

MATLAB for Chemometricians, Linear Algebra for Chemometricians, and Chemometrics I--PCA, or equivalent experience.

Introduction to Multivariate Image Analysis Course Outline

1 Intro to 3-way arrays
  Objects and Variables
  Example Applications
  Structure of Multivariate Images
  Comparison to other sources of 3-way data     

2 Practical Multivariate Image Analysis (MIA)
  Review of Principal Components Analysis
    Scores, loadings and projections
    Unusual samples, residuals and T^2
  Matricizing of images
  Scores images, loadings
  Overlays
  Score/score plots: density
  Links between scores space and the image plane
  Contrast enhancement
  Image SIMCA

3 Multivariate Image Regression analysis (MIR)
  Review of regression: MLR/PCR/PLS
  Scores, loadings
  Image plane and score linking
  Cross validation for images

4 Preprocessing
  Centering and scaling
  Smoothing and derivitizing
  Scatter correction

5 Intro to texture analysis
  Finite Fourier Transform (FFT)
  SVD Spectrum
  Angle Measurement Technique (AMT)
  Kriging
  MIR using texture transforms

6 Alternatives to PCA/PLS
  Multivariate Curve Resolution
  PARAFAC on series of images
  Classical Least Squares
  Positive Matrix Factorization
  Generalized Least Squares and decluttering
  Extended addition model
  Evolving Window Factor Analysis
  Target Factor Analysis

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