Load images

In this chapter, we'll work with sections of a computed tomography (CT) scan from The Cancer Imaging Archive. CT uses a rotating X-ray tube to create a 3D image of the target area.

The actual content of the image depends on the instrument used: photographs measure visible light, x-ray and CT measure radiation absorbance, and MRI scanners measure magnetic fields.

To warm up, use the imageio package to load a single DICOM image from the scan volume and check out a few of its attributes.

This exercise is part of the course

Biomedical Image Analysis in Python

Exercise instructions

Import imageio.
Read in the image "chest-220.dcm" using the imread() function.
Print the type() and shape (number of pixels) of im.

Hands-on interactive exercise

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

# Import ImageIO
import ____

# Load "chest-220.dcm"
im = ____

# Print image attributes
print('Image type:', ____)
print('Shape of image array:', im.____)

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

Current Exercise

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 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