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Introduction to Biomedical Imaging and Machine Learning

This course explores various imaging modalities, components of imaging systems, areas of image analysis, and machine learning techniques for biomedical imaging. Topics include image enhancement, segmentation, registration, 3D reconstruction, visualization, and dimensionality reduction.

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Introduction to Biomedical Imaging and Machine Learning

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  1. Biomedical Image Analysis and Machine LearningBMI 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, Ca2+ concentration …

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

  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. 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

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

  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-Loevetransform (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 m 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. B 2.0 1.5 1.0 0.5 . . . . . . . . . . . . . . . w A 0.5 1.0 1.5 2.0 Linear Discriminant Analysis (From S. Wu’s website)

  19. B 2.0 1.5 1.0 0.5 . . . . . . . . . A . . . . 0.5 1.0 1.5 2.0 . . w Linear Discriminant Analysis (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 …)

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