Biomedical Image Analysis and Machine Learning

BMI 731 Winter 2005 Kun Huang

Department of Biomedical InformaticsOhio 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 analysisFunctional MRI

- PhenotypingMicroscopic imaging for different genotypes, molecular

imaging

- QuantificationCell count, volume rendering, Ca2+ 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)

stackPCA

- 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)

=

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- Dimensionality Reduction- Principal component analysis (PCA)

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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 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

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(From S. Wu’s website)

Linear Discriminant AnalysisB

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.(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 …)