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Biomedical Image Analysis and Machine Learning BMI 731 Winter 2005. Kun Huang Department of Biomedical Informatics Ohio State University. Introduction to biomedical imaging Imaging modalities Components of an imaging system Areas of image analysis Machine learning and image analysis.

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biomedical image analysis and machine learning bmi 731 winter 2005

Biomedical Image Analysis and Machine LearningBMI 731 Winter 2005

Kun Huang

Department of Biomedical Informatics

Ohio State University

slide2

Introduction to biomedical imaging

  • Imaging modalities
  • Components of an imaging system
  • Areas of image analysis
  • Machine learning and image analysis
slide3

Why imaging?

    • Diagnosis

X-ray, MRI, Ultrasound, microscopic imaging (pathology and histology) …

    • Visualization (invasive and noninvasive)

3-D, 4-D

    • Functional analysis

Functional MRI

    • Phenotyping

Microscopic imaging for different genotypes, molecular imaging

    • Quantification

Cell count, volume rendering, Ca2+ concentration …

slide4

Ultrasound

  • Imaging modalities
    • Wavelength
      • Electron microscope
      • X-ray
      • UV
      • Light
      • Ultrasound
    • MRI
    • Fluorescence
    • Multi-spectral
    • Tomography
    • Video
slide5

Components of Imaging System

    • Instrumentation :
      • Electrical engineering, physics, histochemistry …
    • Image generation
      • Sensor technology (e.g., scanner), coloring agents …
    • Image processing and enhancement
      • Both software, hardware, or experimental (dynamic contrast)
    • Image analysis at all levels
      • Image processing, computer vision, machine learning
      • Manual/interactive
    • Image storage and retrieval
      • Database/data warehouse
slide6

Areas of Image Processing and Analysis

    • Image enhancement
      • Color correction, noise removal, contrast enhancement …
    • Feature extraction
      • color, point, edge (line, curves), area
      • cell, tissue type, organ, region
    • Segmentation
    • Registration
    • 3-D reconstruction
    • Visualization
    • Quantization
slide7

Curtersy of Raghu Machiraju

  • Image Analysis and Machine Learning
    • Why machine learning
      • Classification at all levels
        • Pixel, texture, object …
      • Pattern recognition, statistical learning, multivariate analysis …
      • Statistical properties
slide8

PCA

stack

  • Common machine learning techniques
    • Dimensionality reduction
      • Principal component analysis (PCA, SVD, KLT)
      • Linear discriminant analysis (LDA, Fisher’s discriminant)
slide9

Common machine learning techniques

    • Supervised learning

Learning algorithm

?

Classifier

  • Neural network, Support vector machine (SVM), MCMC, Bayesian network …
slide10

Common machine learning techniques

    • Unsupervised learning
  • K-means, K-subspaces, GPCA, hierarchical clustering, vector quantization, …
slide11

Dimensionality Reduction

    • Principal component analysis (PCA)
      • Singular value decomposition (SVD)
      • Karhunen-Loevetransform (KLT)

Basis for P

SVD

slide12

Dimensionality Reduction

    • Principal component analysis (PCA)

=

=

slide13

Dimensionality Reduction

    • Principal component analysis (PCA)

=

Knee point

Optimal in the sense of least square error.

slide14

Principal Component Analysis (PCA)

    • Geometric meaning
      • Fitting a low-dimensional linear model to data

Find m and E such that J is minimized.

slide15

Principal Component Analysis (PCA)

    • Statistical meaning
      • Direction with the largest variance
slide16

Principal Component Analysis (PCA)

    • Algebraic meaning
      • Energy
slide17

Principal Component Analysis (PCA)

    • Application : face recognition (Jon Krueger et. al.)

Average face

Eigenfaces – Principal Components

linear discriminant analysis

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Linear Discriminant Analysis

(From S. Wu’s website)

linear discriminant analysis1

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Linear Discriminant Analysis

(From S. Wu’s website)

slide20

Linear Discriminant Analysis (PCA)

    • Which direction is a good one to pick?
      • Maximize the inter-cluster distance
      • Minimize the intra-cluster distance
    • Compromise : maximize the ratio between the above two distances
slide21

Next time

    • Supervised learning - SVM
    • Unsupervised learning – K-means
    • Spectral clustering

OR

    • CT, Radon transform backprojection
    • MRI
    • Other image processing techniques (filtering, convolution, color and contrast correction …)