1. Biomedical Image Analysis and Machine Learning BMI 731 Winter 2005 Kun Huang Department of Biomedical Informatics Ohio State University

2. -Introduction to biomedical imaging -Imaging modalities -Components of an imaging system -Areas of image analysis -Machine learning and image analysis

3. -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 …

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

5. -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

6. -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

7. -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

8. -Common machine learning techniques -Dimensionality reduction -Principal component analysis (PCA, SVD, KLT) -Linear discriminant analysis (LDA, Fisher’s discriminant) stack PCA

9. -Common machine learning techniques -Supervised learning Learning algorithm Classifier ? -Neural network, Support vector machine (SVM), MCMC, Bayesian network …

10. -Common machine learning techniques -Unsupervised learning -K-means, K-subspaces, GPCA, hierarchical clustering, vector quantization, …

11. -Dimensionality Reduction -Principal component analysis (PCA) -Singular value decomposition (SVD) -Karhunen-Loeve transform (KLT) Basis for P SVD

12. -Dimensionality Reduction -Principal component analysis (PCA) = =

13. -Dimensionality Reduction -Principal component analysis (PCA) = ≈ Knee point Optimal in the sense of least square error.

14. -Principal Component Analysis (PCA) -Geometric meaning -Fitting a low-dimensional linear model to data Find  and E such that J is minimized.

15. -Principal Component Analysis (PCA) -Statistical meaning -Direction with the largest variance

16. -Principal Component Analysis (PCA) -Algebraic meaning -Energy

17. -Principal Component Analysis (PCA) -Application : face recognition (Jon Krueger et. al.) Average face Eigenfaces – Principal Components

18. - 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)

19. 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)

20. -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

21. -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 …)