π Learn how to build AI systems for medical imaging domain by leveraging tools and techniques that I share with you! | π‘ The newsletter is read by people from: Nvidia, Baker Hughes, Harvard, NYU, Columbia University, University of Toronto and more!
SimpleITK for Medical Image Processing
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Hello Reader, Welcome to another edition of PYCAD newsletter where we cover interesting topics in Machine Learning and Computer Vision applied to Medical Imaging. The goal of this newsletter is to help you stay up-to-date and learn important concepts in this amazing field! I've got some cool insights for you below β |
Image Processing Algorithms with SimpleITK
SimpleITK is one of my favorite libraries to deal with medical imaging data.
Here are 4 image processing algorithms that are already implemented in SimpleITK and that are ready to use for your next medical imaging project.
1 - Smoothing (Noise Reduction)
Gaussian smoothing can be used to reduce noise in an image. The degree of smoothing is determined by the Gaussian kernel size (standard deviation).
2 - Contrast Enhancement
Histogram equalization can be used to adjust image intensities to enhance contrast.
3 - Edge Enhancement
Edge enhancement can help to highlight the edges within an image, which can be particularly useful in enhancing the boundaries of structures.
4 - Denoising
Non-local means denoising is a more sophisticated method that can be used to reduce noise while preserving edges
Below you can see a sample code on how to apply these image processing techniques using SimpleITK.
New Medical Imaging Dataset Made Public
New medical imaging dataset made available!
This dataset contains 27 thorax CT scans which contains annotations for airways as well as lungs.
Dataset size: 5Gb
Download dataset here: https://zenodo.org/records/10069289β
Tweet of the Day
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We Can Help You with Your Next Medical Imaging ProjectIf your company or organization is looking to build a machine learning solution for a medical imaging problem, then feel free to reach out to us at: βcontact@pycad.coβ We can help you build a full ML solution from training to deployment with affordable rates! You can check out some of the projects that we worked on here: |
That's it for this week's edition, I hope you enjoyed it!
Machine Learning for Medical Imaging
π Learn how to build AI systems for medical imaging domain by leveraging tools and techniques that I share with you! | π‘ The newsletter is read by people from: Nvidia, Baker Hughes, Harvard, NYU, Columbia University, University of Toronto and more!
Hello Reader, Welcome to another edition of PYCAD newsletter where we cover interesting topics in Machine Learning and Computer Vision applied to Medical Imaging. The goal of this newsletter is to help you stay up-to-date and learn important concepts in this amazing field! I've got some cool insights for you below β What We Learned This Year (Medical Imaging Edition) As this year wraps up, I wanted to share a few quick lessons from the projects we worked on, especially around building web...
Hello Reader, Welcome to another edition of PYCAD newsletter where we cover interesting topics in Machine Learning and Computer Vision applied to Medical Imaging. The goal of this newsletter is to help you stay up-to-date and learn important concepts in this amazing field! I've got some cool insights for you below β Zoom That Works Everywhere If you canβt zoom any pane in your web DICOM viewer, youβre doing extra work for no reason. Think of it like this: when something is small, you bring it...
Hello Reader, Welcome to another edition of PYCAD newsletter where we cover interesting topics in Machine Learning and Computer Vision applied to Medical Imaging. The goal of this newsletter is to help you stay up-to-date and learn important concepts in this amazing field! I've got some cool insights for you below β A Quick Look at Our Volume Measurement Tool One of the tools weβve been working on is a simple way to estimate 3D volumes directly inside the viewer. You start by drawing a...