Biomedical Image Analysis and

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Oct 18, 2013 (3 years and 11 months ago)

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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



-
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, Ca
2+

concentration …


-
Imaging modalities

-
Wavelength

-
Electron microscope

-
X
-
ray

-
UV

-
Light

-
Ultrasound

-
MRI

-
Fluorescence

-
Multi
-
spectral

-
Tomography

-
Video



Ultrasound

-
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



-
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



-
Image Analysis and Machine Learning

-
Why machine learning

-
Classification at all levels

-
Pixel, texture, object …









-
Pattern recognition, statistical learning, multivariate
analysis …

-
Statistical properties




Curtersy of Raghu Machiraju

-
Common machine learning techniques

-
Dimensionality reduction













-
Principal component analysis (PCA, SVD, KLT)

-
Linear discriminant analysis (LDA, Fisher’s discriminant)


stack

PCA

-
Common machine learning techniques

-
Supervised learning


Learning
algorithm

Classifier

?

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


-
Common machine learning techniques

-
Unsupervised learning


-
K
-
means, K
-
subspaces, GPCA, hierarchical
clustering, vector quantization, …


-
Dimensionality Reduction

-
Principal component analysis (PCA)

-
Singular value decomposition (SVD)

-
Karhunen
-
Loeve

transform (KLT)




Basis for
P

SVD

-
Dimensionality Reduction

-
Principal component analysis (PCA)


=

=

-
Dimensionality Reduction

-
Principal component analysis (PCA)


=



Knee point

Optimal in the sense of least square error.

-
Principal Component Analysis (PCA)

-
Geometric meaning

-
Fitting a low
-
dimensional linear model to data



Find
m

and
E

such that
J

is minimized.

-
Principal Component Analysis (PCA)

-
Statistical meaning

-
Direction with the largest variance



-
Principal Component Analysis (PCA)

-
Algebraic meaning

-
Energy



-
Principal Component Analysis (PCA)

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




Average face

Eigenfaces


Principal Components

-

Linear Discriminant Analysis

B

.

2.0





1.5





1.0





0.5

0.5 1.0 1.5 2.0


.

.

.

.

.

.

.

.

.

.

.

.

.

A

w

.

(From S. Wu’s website)

Linear Discriminant Analysis

B

.

2.0





1.5





1.0





0.5

0.5 1.0 1.5 2.0


.

.

.

.

.

.

.

.

.

.

.

.

.

A

w

.

(From S. Wu’s website)

-
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

-
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 …)