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A Taste of Data Mining. Definition. “Data mining is the analysis of data to establish relationships and identify patterns.” practice.findlaw.com/glossary.html . Learning from data. Examples of Learning Problems. Digitized Image  Zip Code

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Presentation Transcript
  • “Data mining is the analysis of data to establish relationships and identify patterns.”practice.findlaw.com/glossary.html.
  • Learning from data.
examples of learning problems
Examples of Learning Problems
  • Digitized Image  Zip Code
  • Based on clinical and demographic variables, identify the risk factors for prostate cancer
  • Predict whether a person who has had one heart attack will be hospitalized again for another.
beneath the blur a look at independent component analysis with respect to image analysis

Beneath the blur: A look at independent component analysis with respect to image analysis

Galen Papkov

Rice University

April 2, 2014

  • Biology
    • Gray vs. White Matter
    • T1 vs. T2
  • How does Magnetic Resonance Imaging work?
  • Theory behind ICA
    • Cocktail party
  • Nakai et al.’s (2004) paper
  • Gray matter consists of cell bodies whereas white matter is made up of nerve fibers


biology cont
Biology (cont.)
  • T2 effect occurs when protons are subjected to a magnetic field
    • T2 time is the time to max dephasing
  • T1 effect is due to the return of the high state protons to the low energy state
    • T1 time is the time to return to equilibrium


how does mri work
How Does MRI work?
  • Protons have magnetic properties
  • The properties allow for resonance
    • process of energy absorption and subsequent relaxation
  • Process:
    • apply an external magnetic field to excite them (i.e. absorb energy)
    • Remove magnetic field so protons return to equilibrium, thereby creating a signal containing information of the “resonanced” area


cocktail party problem
Cocktail Party Problem
  • Scenario:
    • Place a microphone in the center of a cocktail party
    • Observe what the microphone recorded
  • Compare to human brain
independent component analysis ica
Independent Component Analysis (ICA)
  • Goal: to find a linear transformation W (separating matrix) of x (data) that yields an approximation of the underlying signals y which are as independent as possible

x=As(A is the mixing matrix)

s»y=Wx (W»A-1)

  • W is approximated via an optimization method (e.g. gradient ascent)
Application of ICA to MR imaging for enhancing the contrast of gray and white matter (Nakai et al., 2004)
  • Purpose: To use ICA to improve image quality and information deduction from MR images
    • Wanted to use ICA to enhance image quality instead of for tissue classification
  • Subjects: 10 normal, 3 brain tumors, 1 multiple sclerosis
  • Method:
    • Obtain MR images
    • Normalize and take the average of the images
    • Apply ICA
observations w r t ica transformation for normal subjects
Observations w.r.t. ICA transformation for normal subjects
  • IC images after whitening have removed (minimized) “noise”
  • Observe the complete removal of free water
tumor case 1 cont
Tumor Case 1 (cont.)
  • Hazy in location of tumor in original images
  • Less cloudy, but can see involvement of tumor in IC images
tumor case 2 cont
Tumor Case 2 (cont.)
  • Post-radiotherapy and surgery
  • Can clearly see where the tumor was
  • CE image shows residual tumor the best
multiple sclerosis cont
Multiple Sclerosis (cont.)
  • IC1 shows active lesions
  • IC2 shows active and inactive lesions
  • Gray matter intact
  • IC images had smaller variances than original images (per F-test, p<0.001)
    • Sharper/more enhanced images
  • Can remove free water, determine residual tumor or tumor involvement (via disruption of normal matter)
  • Explored increasing the number of components
future research
Future Research
  • Explore ICA’s usefulness with respect to tumors
    • Neutral intensity
    • Tumor involvement in gray and white matter
    • Separate edema from solid part of tumor
  • May help in the removal of active lesions for MS patients
  • Preprocessing method to classify and segment the structure of the brain
  • Hastie, T., Tibshirani, R., & Friedman, J. (2001). The Elements of Statistical Learning: Data mining, inference, and prediction. Springer-Verlag, NY.
  • Nakai, T., Muraki, S., Bagarinao, E., Miki, Y., Takehara, Y., Matsuo, K., Kato, C., Sakahara, H., & Isoda, H. (2004). Application of independent component analysis to magnetic resonance imaging for enhancing the contrast of gray and white matter. NeuroImage, 21(1), 251-260.
  • Stone, J. (2002). Independent component analysis: an introduction. Trends in Cognitive Sciences, 6(2), 59-64.