Slice 3D images
The simplest way to plot 3D and 4D images by slicing them into many 2D frames. Plotting many slices sequentially can create a "fly-through" effect that helps you understand the image as a whole.
To select a 2D frame, pick a frame for the first axis and select all data from the remaining two: vol[0, :, :]
For this exercise, use for loop to plot every 40th slice of vol on a separate subplot. matplotlib.pyplot (as plt) has been imported for you.
This is a part of the course
“Biomedical Image Analysis in Python”
Exercise instructions
- Using
plt.subplots(), initialize a subplots grid with 1 row and 4 columns. - Plot every 40th slice of
volin grayscale. To get the appropriate index, multiplyiiby40. - Turn off the ticks, labels, and frame for each subplot.
- Render the figure.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Plot the images on a subplots array
fig, axes = ____
# Loop through subplots and draw image
for ii in range(4):
im = ____
axes[ii].imshow(____, cmap='gray')
axes[ii].axis(____)
# Render the figure
plt.show()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.