But wait, let's look at some pictures first!
breast_segment takes a Mammography image and detects the largest, connected region (usually the breast).
It outputs a segmentation mask and the coordinates of the rectangular bounding box.
Now, the images are easily explained:
- First image: Input Image
- Second image: Segmentation mask (computed, boolean)
- Third image: Bounding Box (computed)
- Fourth image: Overlay Visalization of the Segmentation
breast_segment should be available on your python path.
You could achieve this like so:
import sys
sys.path.append('PATH_TO_BREAST_SEGMENT')Then, import and use. Let's say that we've loaded an image into the variable im.
It should be a NumPy Array.
from breast_segment import breast_segment
mask, bbox = breast_segment(im)Done! You may want to visualize it to check if it worked. Let's use matplotlib:
from matplotlib import pyplot as plt
%matplotlib inlineVisualize the original image (you probably know how to do that):
f, ax = plt.subplots(1, figsize=(12, 12)) # adjust the figure size
# set the correct window and color map. yours may differ.
# radiologists like gray, not understandably
ax.imshow(im, vmin=0, vmax=4096, cmap='gray')Have a look at the segmentation mask:
f, ax = plt.subplots(1, figsize=(12, 12))
ax.imshow(mask, vmin=0, vmax=1, cmap='inferno') # use inferno colormap for dramatisationCreate an overlay visualization like in the fourth image above:
f = plt.figure(figsize=(12, 12))
ax = plt.subplot(111)
ax.imshow(im, vmin=0, vmax=4096, cmap='gray')
ax.imshow(mask, alpha=.3, cmap='inferno') # alpha controls the transparencyDone!
Showing the bounding box like in the third image above is a little more code but also easy. Check out the complete example (see below) for the full story!
If you want to tweak some parameters, look no further:
mask, bbox = breast_segment(image, scale_factor=0.25, threshold=3900, felzenzwalb_scale=0.15)Input Parameters
| Parameter | Type | Explanation |
|---|---|---|
| image | NumPy Array, two-dimensional | The Input Mammography to segment. |
| scale_factor | float | Downscaling factor. The segmentation will be computed on this smaller image. Default: 0.25 (25% of original size) |
| threshold | integer | The maximum cut-off for noisy (scanned) Mammography images. Values above are treated as noise and ignored. Default: 3900 (values > 3900 will be set to 0) |
| felzenzwalb_scale | float | Scale Parameter for Felzenzwalb's Algorithm. Check out the respctive skimage docs for more information. Default: 0.15 (tested on DDSM) |
Output Parameters
| Parameter | Type | Explanation |
|---|---|---|
| mask | NumPy Array, two-dimensional, boolean | The computed segmentation mask. true (1) values for breast, false for everything else. |
| bbox | 4-element Tuple | The coordinates of the rectangular bounding box of the segmentation. The tuple consists of (min_row, min_col, max_row, max_col) where min_row is the starting row and max_row the ending row of the bounding box. Same for min_col and max_col, only in columns (you guessed it). |
The above images were generated in an IPython Notebook which resides examples/ (ddsm_examples.ipynb).
Check out the full notebook for a complete walkthrough!
The Mammography image is from the Digital Database for Screening Mammography (DDSM).
To my knowledge, it's one of the biggest public mammography databases. However, you will
need some more tools to open and process the weird LJPEG format and their associated
metadata, which I conveniently provide:
breast_segment is basically a simple application of the Felzenzwalb Algorithm in skimage
with some tweaking of parameters and thresholding for noise-reduction. Additionally,
Felzenzwalb's Algorithm is only applied to a downscaled version of the image to reduce
computation time.
Sometimes, breast_segment wrongly detects the background as "breast" and creates an inverted
segmentation. There is a slightly hacky mechanism to detect that, in which case it simply selects
the second largest region (instead of the largest) as breast.
In other cases, it simply fails dramatically. Tweaking the threshold and Felzenzwalb Scale to some optimum values for the given Mammography images, dependent e.g. on the type of scanner, can help in some cases.
I had been trying to apply Neural Networks to Mammographies by training them to differentiate between bening and malign images of the DDSM database (spoiler: it didn't work). In trying to improve the preprocessing (reducing the noise), I developed this automatic segmentation. Feel free to [contact me] if you want to hear the full story!
As I'm currently moving away from Machine Learning, I decided to open-source it to spare you some time and nerves. Good luck!
Feel free to file an issue on this Repo or contact me (Oliver Eidel), I'm happy to help!
MIT