Rotations

In cases where an object is angled or flipped, the image can be rotated. Using ndi.rotate(), the image is rotated from its center by the specified degrees from the right horizontal axis.

For this exercise, shift and rotate the brain image (im) so that it is roughly level and "facing" the right side of the image.

This exercise is part of the course

Biomedical Image Analysis in Python

Exercise instructions

Shift im towards the center: 20 pixels left and 20 pixels up.
Use ndi.rotate to turn xfm 30 degrees downward. Set reshape=False to prevent the image shape from changing.
Plot the original and transformed images.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Shift the image towards the center
xfm = ____

# Rotate the shifted image
xfm = ndi.rotate(____, angle=____, reshape=____)

# Plot the original and rotated images
fig, axes = plt.subplots(2, 1)
axes[0].imshow(im)
____
format_and_render_plot()

Edit and Run Code

This exercise is part of the course

Biomedical Image Analysis in Python

IntermediateSkill Level

4.7+

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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 data Exercise 2: Load images Exercise 3: Metadata Exercise 4: Plot images Exercise 5: N-dimensional images Exercise 6: Stack images Exercise 7: Load volumes Exercise 8: Field of view Exercise 9: Advanced plotting Exercise 10: Generate subplots Exercise 11: Slice 3D images Exercise 12: Plot other views

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.

Exercise 1: Image intensities Exercise 2: Intensity Exercise 3: Histograms Exercise 4: Masks Exercise 5: Create a mask Exercise 6: Apply a mask Exercise 7: Tune a mask Exercise 8: Filters Exercise 9: Filter convolutions Exercise 10: Filter functions Exercise 11: Smoothing Exercise 12: Feature detection Exercise 13: Detect edges (1)Exercise 14: Detect edges (2)

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.

Exercise 1: Objects and labels Exercise 2: Segment the heart Exercise 3: Select objects Exercise 4: Extract objects Exercise 5: Measuring intensity Exercise 6: Measure variance Exercise 7: Separate histograms Exercise 8: Measuring morphology Exercise 9: Calculate volume Exercise 10: Calculate distance Exercise 11: Pinpoint center of mass Exercise 12: Measuring in time Exercise 13: Summarize the time series Exercise 14: Measure ejection fraction

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.

Exercise 1: Spatial transformations Exercise 2: Translations Exercise 3: Rotations

Current Exercise

Exercise 4: Affine transform Exercise 5: Resampling and interpolation Exercise 6: Resampling Exercise 7: Interpolation Exercise 8: Comparing images Exercise 9: Mean absolute error Exercise 10: Intersection of the union Exercise 11: Normalizing measurements Exercise 12: Identifying potential confounds Exercise 13: Testing group differences Exercise 14: Normalizing metrics