Stack images
Image "stacks" are a useful metaphor for understanding multi-dimensional data. Each higher dimension is a stack of lower dimensional arrays.
In this exercise, we will use NumPy's stack() function to combine several 2D arrays into a 3D volume. By convention, volumetric data should be stacked along the first dimension: vol[plane, row, col].
Note: performing any operations on an ImageIO Image object will convert it to a numpy.ndarray, stripping its metadata.
This is a part of the course
“Biomedical Image Analysis in Python”
Exercise instructions
- Import
imageioandnumpy(asnp). - Load "chest-220.dcm", "chest-221.dcm", and "chest-222.dcm".
- Create a 3D volume using
np.stack(). Set the stackingaxisto 0. - Print the
shapeattribute ofvol.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Import ImageIO and NumPy
____
import ____ as ____
# Read in each 2D image
im1 = imageio.imread('chest-220.dcm')
im2 = ____
im3 = ____
# Stack images into a volume
vol = np.stack(____)
print('Volume dimensions:', ____)This exercise is part of the course
Biomedical Image Analysis in Python
Learn the fundamentals of exploring, manipulating, and measuring biomedical image data.
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.
Exercise 1: Image dataExercise 2: Load imagesExercise 3: MetadataExercise 4: Plot imagesExercise 5: N-dimensional imagesExercise 6: Stack imagesExercise 7: Load volumesExercise 8: Field of viewExercise 9: Advanced plottingExercise 10: Generate subplotsExercise 11: Slice 3D imagesExercise 12: Plot other viewsWhat is DataCamp?
Learn the data skills you need online at your own pace—from non-coding essentials to data science and machine learning.