<jupyter_start><jupyter_text>Insights into Microscopy Images  Learning Objectives* Learn how to **run** cells in **a Jupyter Notebook*** Run a segmentation example that demonstrates **ITK**'s ability to provide **insight into images*** Understand the **purpose and capabilities** of the toolkit  Jupyter Notebooks These are [Jupyter Notebooks](https://jupyter.org/), an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text.To run cells in the notebook, press *shift + enter*.For more information, see the [Notebook Help](https://nbviewer.jupyter.org/github/ipython/ipython/blob/3.x/examples/Notebook/Index.ipynb)  What is in my image?<jupyter_code>import itk

image = itk.imread('data/6001240.tif')
from itkwidgets import view

view(image, gradient_opacity=0.5)<jupyter_output><empty_output><jupyter_text>- Data source: https://doi.org/10.17867/10000125- Article: https://doi.org/10.1371/journal.pbio.3000388- Link: https://idr.openmicroscopy.org/webclient/?show=image-6001240- Organism:	Mus musculus- Study Title:	NesSys: a new set of tools for the automated detection of nuclei within intact tissues and dense 3D cultures- Imaging Method:	confocal microscopy- Publication Title:	NesSys: a novel method for accurate nuclear segmentation in 3D- Publication Authors:	<NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>- Protocol 1:	growth protocol - Mouse blastocysts (E3.5)- Protocol 2:	Immunostaining: LaminB1 antibody: ab16048 (dilution 1:1000).- Protocol 3:	Imaged with an Sp8 leica confocal microscope and HC PL APO 40x/1.30 Oil CS2 objective. Nuclear features were extracted in PickCells using manually segmented nuclei in Nessys editor.  Can I reduce the noise?<jupyter_code>import numpy as np

image_float = image.astype(np.float32)

denoised = itk.curvature_flow_image_filter(image_float,
                                           number_of_iterations=10)
denoised = itk.median_image_filter(denoised,
                                   radius=1)
denoised = itk.smoothing_recursive_gaussian_image_filter(denoised,
                                   sigma=0.5)

view(denoised, gradient_opacity=0.5)<jupyter_output><empty_output><jupyter_text>History of ITK  Insight into Images![Visible Human Head Rendering](data/VisibleHumanHeadRender.png)http://itk.org  HistoryIn 1999, the US National Institute of Health’s (NIH) National Library of Medicine (NLM) started a project to support the Visible Human Project.![Visible Human Logo](data/VisibleHumanLogo.png)![Visible Human Slice](data/VisibleHumanSlice.png)  Goals* Collect best-of-the-best image analysis algorithms for reproducible science.* Provide a software resource for teaching medical image analysis algorithms.* Establish a foundation for future research.* Support commercial applications.* Create conventions for future work.* Grow a self-sustaining community of software users and developers.  Continued Development* Development has progressed since 1999* Contributions from over 300 developers* Over 1.7 million lines of code![ITK contributors](data/itk-contributors.png)ITK contributors locations for the 4.8 and 4.9 releases.  Features  N-Dimensional Image Filtering![Filtering](data/Filtering.png)  Filtering Algorithms Classes* Fast marching methods* Convolution* Image gradient* Denoising* Thresholding* Mathematical morphology* Smoothing* Image features* Image statistics* Bias correction* Image grid operations* ....  Segmentation"Image segmentation is the process of partitioning a digital image into multiple segments, i.e. sets of pixels. The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze."Source: [Wikipedia](https://en.wikipedia.org/wiki/Image_segmentation)  Segmentation![Segmentation](data/SegmentationLevelSet.png)  Segmentation![Segmentation](data/Segmentation2.png)  Registration"Image registration is the process of transforming different sets of data into one coordinate system. [...] Registration is necessary in order to be able to compare or integrate the data obtained from these different measurements."Source: [Wikipedia](https://en.wikipedia.org/wiki/Image_registration)  Registration![Registration](data/Registration.png)<jupyter_code>image_da = itk.xarray_from_image(image)

image_da<jupyter_output><empty_output>
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