Alternate header for print version

Processing raw micrographs
Electron microscope data has signal-to-noise (SNR) ratios and a raw image will have some level of noise and contrast shift that needs to be processed into images with high acuity.

An Algorithm for Enhancing the Image Contrast of Electron Tomography:

import glob, os
from import imread
rawImage = imread('/data/rawimage.png')
from skimage import io
from matplotlib import pyplot as plt
from matplotlib.pyplot import figure

figure(figsize=(12, 18), dpi=80)

tile = rawImage[:256,:256]

figure(figsize=(5, 5), dpi=80)
from __future__ import print_function
from ipywidgets import interact, interactive, fixed, interact_manual
import ipywidgets as widgets
from skimage.exposure import adjust_log, equalize_adapthist
from skimage.filters import median

def imageProcessing(contrastAmount = 0, medianAmount = 0, logAmount = 0, image = None):

 image = image.astype('float32')
 image = image/image.max()

 contrastAdjusted = equalize_adapthist(image.copy())
 image = contrastAmount*contrastAdjusted+(1-contrastAmount)*image

 medianAdjusted = median(image.copy())
 image = medianAmount*medianAdjusted+(1-medianAmount)*image

 logAdjusted = adjust_log(image.copy())
 image = logAmount*logAdjusted+(1-logAmount)*image

 figure(figsize=(5, 5), dpi=80)


func = lambda contrastAmount, medianAmount, logAmount: imageProcessing(contrastAmount, medianAmount,logAmount, image=tile)
interact(func, contrastAmount=(0,1,.1), medianAmount = (0,1,.1),logAmount = (0,1,.1));

For developers
Markdown and notebook formatted files contatin detailed explanations of the tools used and how they are used. They can also execute on the locations where data is available and required resources.

Launch an interactive demo
*This button will launch an interactive Jupyter session that will allow you to view some of the complex image processing necessary to build these image volumes.

Project Jupyter (, provides a comprehensive platform to create, test, distribute, reproduce, and deploy scientific programming in custom environments. We use Jupyter-based Notebooks and BinderHubs to allow public users to test many of the complex computations required to produce this AD data.