3-D image processing with scikit-image and the scientific Python ecosystem

Talk given at ICTMS 2015 (Quebec City). A presentation on how to use the Python package scikit-image for processing 3-D data such as X-ray tomography images.

Transcript

1. 3-D image processing with scikit-image and the scientic Python ecosystem Emmanuelle Gouillart joint Unit CNRS/Saint-Gobain SVI @EGouillart scikit-image team
2. What is scikit-image? An open-source (BSD) generic image processing library for the Python language (and NumPy data arrays)
3. What is scikit-image? An open-source (BSD) generic image processing library for the Python language (and NumPy data arrays) for 2D & 3D images simple API & gentle learning curve
4. Python: a versatile & modern language A modern language (1989) concise interpreted >>> f o r i in range (3): ... p r i n t ( i **2) ... 0 1 4 x xkcd.com
5. Python: a versatile & modern language A modern language (1989) concise interpreted >>> f o r i in range (3): ... p r i n t ( i **2) ... 0 1 4 x used by various actors web dev scripting scientific computing education... xkcd.com
6. NumpPy: Python objects for numerical arrays Multi-dimensional numerical data container (based on compiled code) + utility functions to create/manipulate them >>> a = np.random. r a n d o m i n t e g e r s (0, 1, (2, 2, 2)) >>> a a r r a y ([[[0 , 1], [1, 0]], [[0, 0], [0, 1]]]) >>> a. shape , a. dtype ((2, 2, 2), dtype ( ’ int64 ’ )) x
7. NumpPy: Python objects for numerical arrays Multi-dimensional numerical data container (based on compiled code) + utility functions to create/manipulate them >>> a = np.random. r a n d o m i n t e g e r s (0, 1, (2, 2, 2)) >>> a a r r a y ([[[0 , 1], [1, 0]], [[0, 0], [0, 1]]]) >>> a. shape , a. dtype ((2, 2, 2), dtype ( ’ int64 ’ )) x Efficient and versatile data access indexing and slicing fancy indexing
8. The Scientific Python ecosystem Signal processing Specialized modules Visualization Interpreters and IDEs
9. The Scientific Python ecosystem Integrated distributions Signal processing Specialized modules Visualization Interpreters and IDEs
10. First steps with scikit-image >>> from skimage import data , measure >>> im = data . b i n a r y b l o b s (128 , n dim =3, v o l u m e f r a c t i o n =0.2) >>> im. shape # 3-D array (128 , 128, 128) >>> l a b e l s = measure . l a b e l (im) >>> type(im), type( l a b e l s ) # images are numpy arrays (<type ’numpy . ndarray ’ >, <type ’numpy . ndarray ’ >) >>> l a b e l s [0, 0, 0] # pixels are array elements 0 x Visualization with Mayavi [Ramachandran and Varoquaux, 2011] Images are NumPy arrays Pixels are array elements Simple API relying on functions new = function(image) Most functions work for 2D & 3D Optional parameters: keyword arguments
11. Datasheet Package statistics http://scikit-image.org/ Release 0.11 (1 - 2 release per year) Among 1000 best ranked packages on PyPi Development model Mature algorithms Only Python + Cython code for easier maintainability Focus on good practices: testing, documentation, version control Hosted on GitHub: thorough code reivew + continuous integration Core team of 5 − 10 persons (close to applications)
12. Filtering: transforming image data skimage.filters, skimage.exposure, skimage.restoration
13. Denoising tomography images In-situ imaging of phase separation in silicate melts [Bouttes et al., 2014, Bouttes et al., 2015] From basic (generic) to advanced (specific) filters
14. Denoising tomography images Histogram of pixel values From basic (generic) to advanced (specific) filters bilateral = restoration . denoise bilateral (dat) bilateral = restoration . denoise bilateral (dat, sigma range=2.5, sigma spatial=2) tv = restoration . denoise tv chambolle (dat, weight=0.5)
15. Segmentation: labelling regions skimage.segmentation
16. Example: segmentation of low-constrast regions In-situ imaging of glass batch reactive melting [Gouillart et al., 2012] Non-local means denoising to preserve texture Histogram-based markers extraction Random walker segmentation Non-local means: average similar patches [Buades et al., 2005] Random walker:anisotropic diffusion from markers [Grady, 2006] Random walker less sensitive to noise than watershed, but slower
17. Mathematical morphology skimage.morphology: binary + grayscale morphology dilation, erosion, closing, opening several structural elements remove small objects watershed
18. Measures on images skimage.measure
19. Extracting features skimage.feature, skimage.filters
20. Feature extraction followed by classification Combining scikit-image and scikit-learn Extract features (skimage.feature) Pixels intensity values (R, G, B) Local gradients More advanced descriptors: HOGs, Gabor, ... Train classifier with known regions here, random forest classifier Classify pixels
21. Other modules skimage.transform: geometrical transformations scale, zoom, rotate, swirl, warp, ... skimage.exposure: manipulate range of color/grayscales . . .
22. Visualizations using Mayavi [Ramachandran and Varoquaux, 2011]
23. Goodies from the ecosystem IPython notebook: code, text, interactive widgets... http://tonysyu.github.io/ ipython-jupyter-widgets-an-image-convolution-demo.html
24. Goodies from the ecosystem joblib: easy simple parallel computing + lazy re-evaluation >>> from j o b l i b import P a r a l l e l , d e l a y e d >>> from math import s q r t >>> P a r a l l e l ( n j o b s =1)( d e l a y e d ( s q r t )( i **2) f o r i in range (10)) [0.0 , 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0] x Familiar with this mess? from skimage import f i l t e r s # Comment to save some time # filter_im = filters.median(im) # binary_im = filters. threshold_otsu (filter_im) v a l u e s = np. unique (im) x
25. Goodies from the ecosystem joblib: easy simple parallel computing + lazy re-evaluation Familiar with this mess? from skimage import f i l t e r s # Comment to save some time # filter_im = filters.median(im) # binary_im = filters. threshold_otsu (filter_im) v a l u e s = np. unique (im) x >>> from j o b l i b import Memory >>> mem = Memory( c a c h e d i r = ’ /tmp/ j o b l i b ’ ) >>> square = mem. cache (np. square ) >>> b = square (a) [Memory] C a l l i n g square ... square ( a r r a y ([[ 0., 0., 1.], [ 1., 1., 1.], [ 4., 2., 1.]])) s q u a r e - 0... s , 0.0 min >>> c = square (a) >>> # The above call did not trigger an evaluation x
26. A few tricks for large images I/O: tomography images might not fit into memory use memory mapping of different file formats (raw binary with NumPy, hdf5 with pytables) for input data, and intermediate results Divide into blocks: use util.view as blocks to iterate conveniently over blocks Use NumPy wisely: slicing over an array makes no copy
27. Wish list (hopefully coming soon) Port to 3-D a few only 2-D functions: skeletonization, region properties, clear border graph cut segmentation Option for embarassingly-parallel processing of image blocks ... your suggestions?
28. Getting started: finding documentation
29. Gallery of examples
30. Getting started: finding documentation
31. Try it out! http://scikit-image.org/ Feedback welcome github.com/scikit-image/scikit-image Please cite the paper Let’s talk about scikit-image @EGouillart
32. Bibliography I Bouttes, D., Gouillart, E., Boller, E., Dalmas, D., and Vandembroucq, D. (2014). Fragmentation and limits to dynamical scaling in viscous coarsening: An interrupted in situ x-ray tomographic study. Physical review letters, 112(24):245701. Bouttes, D., Lambert, O., Claireaux, C., Woelffel, W., Dalmas, D., Gouillart, E., Lhuissier, P., Salvo, L., Boller, E., and Vandembroucq, D. (2015). Hydrodynamic coarsening in phase-separated silicate melts. Acta Materialia, 92:233–242. Buades, A., Coll, B., and Morel, J. (2005). A non-local algorithm for image denoising. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005, pages 60–65.
33. Bibliography II Gouillart, E., Toplis, M. J., Grynberg, J., Chopinet, M.-H., Sondergard, E., Salvo, L., Su´ ery, M., Di Michiel, M., and Varoquaux, G. (2012). In situ synchrotron microtomography reveals multiple reaction pathways during soda-lime glass synthesis. Journal of the American Ceramic Society, 95(5):1504–1507. Grady, L. (2006). Random walks for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1768–1783. Ramachandran, P. and Varoquaux, G. (2011). Mayavi: 3d visualization of scientific data. Computing in Science & Engineering, 13(2):40–51.