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Ben Bolstad has completed

Biomedical Image Analysis in Python

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4 hours
4,400 XP
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Course Description

The field of biomedical imaging has exploded in recent years - but for the uninitiated, even loading data can be a challenge! In this introductory course, you'll learn the fundamentals of image analysis using NumPy, SciPy, and Matplotlib. You'll navigate through a whole-body CT scan, segment a cardiac MRI time series, and determine whether Alzheimer’s disease changes brain structure. Even if you have never worked with images before, you will finish the course with a solid toolkit for entering this dynamic field.
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  1. 1

    Exploration

    Free

    Prepare to conquer the Nth dimension! To begin the course, you'll learn how to load, build and navigate N-dimensional images using a CT image of the human chest. You'll also leverage the useful ImageIO package and hone your NumPy and matplotlib skills.

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    Image data
    50 xp
    Load images
    100 xp
    Metadata
    100 xp
    Plot images
    100 xp
    N-dimensional images
    50 xp
    Stack images
    100 xp
    Load volumes
    100 xp
    Field of view
    50 xp
    Advanced plotting
    50 xp
    Generate subplots
    100 xp
    Slice 3D images
    100 xp
    Plot other views
    100 xp
  2. 2

    Masks and Filters

    Cut image processing to the bone by transforming x-ray images. You'll learn how to exploit intensity patterns to select sub-regions of an array, and you'll use convolutional filters to detect interesting features. You'll also use SciPy's ndimage module, which contains a treasure trove of image processing tools.

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  3. 3

    Measurement

    In this chapter, you'll get to the heart of image analysis: object measurement. Using a 4D cardiac time series, you'll determine if a patient is likely to have heart disease. Along the way, you'll learn the fundamentals of image segmentation, object labeling, and morphological measurement.

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  4. 4

    Image Comparison

    For the final chapter, you'll need to use your brain... and hundreds of others! Drawing data from more than 400 open-access MR images, you'll learn the basics of registration, resampling, and image comparison. Then, you'll use the extracted measurements to evaluate the effect of Alzheimer's Disease on brain structure.

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Datasets

RSNA Hand RadiographOASIS Brain MeasurementsSunnybrook Cardiac MRITCIA Chest CT (Sample)

Collaborators

Collaborator's avatar
Lore Dirick
Collaborator's avatar
Becca Robins
Collaborator's avatar
Sara Snell

Prerequisites

Intermediate Python
Stephen Bailey HeadshotStephen Bailey

Lead Data Scientist

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