Image Segmentation is one of the most important steps in most imaging analysis pipelines. It separates between the background and the features of our images. It can also determine the number of distinct features and their location. Our ability to segment determines what we can analyze. We’ll look at a basic but complete segmentation pipeline with scikit-image. You can see the result in the title image where we segment four cells. First, we will need to threshold the image into a binary version where the background is 0 and the foreground is 1.
import numpy as np import matplotlib.pyplot as plt from skimage import io, filters, morphology, color image = io.imread("example_image.tif") # Load Image threshold = filters.threshold_otsu(image) # Calculate threshold image_thresholded = image > threshold # Apply threshold # Show the results fig, ax = plt.subplots(1, 2) ax.imshow(image, 'gray') ax.imshow(image_thresholded, 'gray') ax.set_title("Intensity") ax.set_title("Thresholded")
We calculate the threshold with the
threshold_otsu function and apply it with a boolean operator. This threshold method works very well but there are two problems. First, there are very small particles that have nothing to do with our cell. To take care of those, we will apply morphological erosion. Second, there are holes in our cells. We will close those with morphological dilation.
# Apply 2 times erosion to get rid of background particles n_erosion = 2 image_eroded = image_thresholded for x in range(n_erosion): image_eroded = morphology.binary_erosion(image_eroded) # Apply 14 times dilation to close holes n_dilation = 14 image_dilated = image_eroded for x in range(n_dilation): image_dilated = morphology.binary_dilation(image_dilated) # Apply 4 times erosion to recover original size n_erosion = 4 image_eroded_two = image_dilated for x in range(n_erosion): image_eroded_two = morphology.binary_erosion(image_eroded_two) fig, ax = plt.subplots(2,2) ax[0,0].imshow(image_thresholded, 'gray') ax[0,1].imshow(image_eroded, 'gray') ax[1,0].imshow(image_dilated, 'gray') ax[1,1].imshow(image_eroded_two, 'gray') ax[0,0].set_title("Thresholded") ax[0,1].set_title("Eroded 2x") ax[1,0].set_title("Dilated 14x") ax[1,1].set_title("Eroded 4x")
Erosion turns any pixel black that is contact with another black pixel. This is how erosion can get rid of small particles. In our case we need to apply erosion twice. Once those particles disappeared, we can use dilation to close the holes in our cells. To close all the holes, we have to slightly over dilate, which makes the cells slightly bigger than they actually are. To recover the original morphology we apply some more erosions. Here is an example that shows how erosion and dilation work in detail. It also illustrates what being “in contact” with another pixel means by default.
cross = np.array([[0,0,0,0,0], [0,0,1,0,0], [0,1,1,1,0], [0,0,1,0,0], [0,0,0,0,0]], dtype=np.uint8) cross_eroded = morphology.binary_erosion(cross) cross_dilated = morphology.binary_dilation(cross) fig, ax = plt.subplots(1,3) ax.imshow(cross, 'gray') ax.imshow(cross_eroded, 'gray') ax.imshow(cross_dilated, 'gray') ax.set_title("Cross") ax.set_title("Cross Eroded") ax.set_title("Cross Dilated")
Now we are essentially done segmenting foreground and background. But we also want to assign distinct labels to our objects.
labels = morphology.label(image_eroded_two) labels_rgb = color.label2rgb(labels, colors=['greenyellow', 'green', 'yellow', 'yellowgreen'], bg_label=0) image.shape # (342, 382) labels.shape # (342, 382) fig, ax = plt.subplots(2,2) ax[0,0].imshow(labels==1, 'gray') ax[0,1].imshow(labels==2, 'gray') ax[1,0].imshow(labels==3, 'gray') ax[1,1].imshow(labels_rgb) ax[0,0].set_title("label == 1") ax[0,1].set_title("label == 2") ax[1,0].set_title("label == 3") ax[1,1].set_title("All labels RGB")
morphology.label to generate a label for each connected feature. This returns an array that has the same shape as our original image but the pixels are no longer zero or one. The background is zero but each feature gets its own integer. All pixels belonging to the first label are equal to 1, pixels of the second label equal to 2 and so on. To visualize those labels all in one image, we call
color.label2rgb to get color representations for each label in RGB space. And that’s it.
Segmentation is crucial for image analysis and I hope this tutorial got you on a good way to do your own segmentation with scikit-image. This pipeline is not perfect but illustrates the concept well. There are many more functions in the morphology module to filter binary images, but they all come down to a sequence of erosions and dilations. If you want to adapt this approach for your own images, I would recommend to play around with the number of erosions and dilations. Let me know how it worked for you.