Alex de Siqueira: An Overview of 3D Image Processing Using Scikit-image
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- Transcript: https://github.com/data-umbrella/event-transcripts/blob/main/2022/67-alex-image.md
- Meetup Event: https://www.meetup.com/data-umbrella/events/288669123/
- Video: https://youtu.be/tPNUX5NxlVY
- Jupyter notebook: https://github.com/scikit-image/skimage-tutorials/tree/main/workshops/2022-data-umbrella
- scikit-image tutorials: https://github.com/scikit-image/skimage-tutorials/
- scikit-image website: https://scikit-image.org/
- scikit-image on GitHub: https://github.com/scikit-image/scikit-image
- Book: The Image Processing Handbook, Sixth Edition, John C. Russ. https://www.cambridge.org/core/journals/microscopy-and-microanalysis/article/abs/image-processing-handbook-sixth-edition-john-c-russ-crc-press-boca-raton-fl-2011-972-pages-isbn-1439840450hardcover/0EE71F73E422212792770A709C38F210
- Contributing to scikit-image: https://scikit-image.org/docs/stable/contribute.html
- https://forum.image.sc/
- https://discuss.scientific-python.org/
- https://scientific-python.org/calendars/
- Hacktoberfest issues: https://github.com/scikit-image/scikit-image/issues?q=is%3Aopen+is%3Aissue+label%3A%22%3Abeer%3A+Hacktoberfest%22
- ITK’s ikwidgets: https://github.com/InsightSoftwareConsortium/itkwidgets
This presentation will introduce how to analyze three dimensional stacked and volumetric images in Python, mainly using scikit-image. Here we will study how to preprocess data using filtering, binarization and segmentation techniques. Also, we will inspect, count and measure attributes of objects and regions of interest in the data. We will finish the tutorial presenting tools to visualize 3D data.
## Timestamps
00:00 Data Umbrella Introduction
03:38 Speaker Introduction - Alex de Siqueira
05:09 Introduction to scikit-image for 3D image analysis
06:05 What is scikit-image?
07:12 Checking the system
07:39 Importing the base Scientific Python ecosystem (numpy, matplotlib, scipy)
08:29 Introduction to 3D image processing (grayscale v. multichannel)
10:25 Resolution within dimensions
11:14 Submodule + demo: skimage.io - utilities for reading and writing images
18:06 skimage.exposure - evaluating or changing the exposure of an image (reduce noise)
20:46 skimage.filters - apply filters to an image
22:57 skimage.transform - transforms & warping (downsampling example)
25:56 skimage.util - other utility functions
29:40 Thresholding - extract regions and see images (soft v. hard thresholding)
31:51 skimage.morphology - binary and grayscale morphology (use for improving image features)
33:36 skimage.measure - measuring image or region properties
37:53 skimage.feature - extract features from an image
38:45 skimage.segmentation - identification of regions of interest (supervised v. unsupervised)
47:21 Visualization
48:50 Going beyond - resources linked
50:27 Q&A - Segmentation and counting of objects
52:57 Q&A - Community calls for scikit-image
54:57 Q&A - Best way for people to get started
56:47 Q&A - Supported image formats
57:48 Q&A - Best way for community to get in touch, events (Hacktoberfest, PyData Global Online)
Alex is a researcher working with outreach and programming on data science and computer vision. He is a maintainer of scikit-image, a collection of algorithms for image processing that is part of the scientific Python ecosystem. Alex is an open source and free software enthusiast since his first interaction with Linux in the 2000s, contributing to several projects and events worldwide.
- LinkedIn: https://www.linkedin.com/in/alexdesiqueira/
- Twitter: https://twitter.com/alexdesiqueira
- GitHub: https://github.com/alexdesiqueira/
#python #computervision #biology
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