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.

Image data

Load images

Metadata

Plot images

N-dimensional images

Stack images

Load volumes

Field of view

Advanced plotting

Generate subplots

Slice 3D images

Plot other views

Exploration

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.

Image intensities

Intensity

Histograms

Masks

Create a mask

Apply a mask

Tune a mask

Filters

Filter convolutions

Filter functions

Smoothing

Feature detection

Detect edges (1)

Detect edges (2)

Masks and Filters

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.

Objects and labels

Segment the heart

Select objects

Extract objects

Measuring intensity

Measure variance

Separate histograms

Measuring morphology

Calculate volume

Calculate distance

Pinpoint center of mass

Measuring in time

Summarize the time series

Measure ejection fraction

Measurement

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.

Spatial transformations

Translations

Rotations

Affine transform

Resampling and interpolation

Resampling

Interpolation

Comparing images

Mean absolute error

Intersection of the union

Normalizing measurements

Identifying potential confounds

Testing group differences

Normalizing metrics

Image Comparison

RSNA Hand Radiograph

OASIS Brain Measurements

Sunnybrook Cardiac MRI

TCIA Chest CT (Sample)

Chest CT Volume

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.

Intermediate Python

Learn the fundamentals of exploring, manipulating, and measuring biomedical image data. 

Biomedical Image Analysis in Python

Görüntü İşleme Python'da

Filter convolutions

Biomedical Image Analysis in Python

Uygulamalı etkileşimli egzersiz