Welcome to the Xtra Library for
Thermo Scientific Amira, Avizo and PerGeos Software

Below you will find a collection of add-ons (recipes, scripts, demos,…) that will help you improve your day-to-day use of Amira-Avizo and PerGeos Software and make you gain both time and efficiency.
Use the Search field to look for specific keywords related to your domain of interest. The different filters also help you target specific resources.

2D SEM  (White) Grain Segmentation

2D SEM (White) Grain Segmentation

Recipe (hxrecipe), Data

This recipe segments and calculate the physical attributes of the white grains in the 2D SEM image

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Depthwise Volume Fraction

Depthwise Volume Fraction

Python (pyscro)

This module allows the user to compute the volume fraction per separated region depending on depth from the surface of a segmented object.

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View Manager

View Manager

Tcl

This module allows storing and retrieving camera settings for viewer windows to be able to switch easily between different view presets.

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Auto-Crop script module

Auto-Crop script module

Tcl

Automatically crop your volume data to reduce its size.

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Create Label Annotations

Create Label Annotations

Tcl

This module creates label annotations from the result of a Label Analysis with the help of the Measurement tool.

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Export Spatial Graph to MS Excel XML document

Export Spatial Graph to MS Excel XML document

Recipe (hxrecipe), Tcl, Demo

An Amira-Avizo recipe to export to a XML document which can be used in MS Excel for further analysis.

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Length Distribution Analysis

Length Distribution Analysis

Recipe (hxrecipe), Project (hx, pgo), Demo, Data

This tutorial demonstrated how to quantify thickness distribution along the length

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Recipe Sequencer

Recipe Sequencer

Recipe (hxrecipe), XPand module, Data

Module for sequencing a recipe on each individual isolated label from a label field.

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Refine Surface

Refine Surface

XPand module

Module for refining a surface.

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Porosity Clustering Analysis and Distance to Surface

Porosity Clustering Analysis and Distance to Surface

Recipe (hxrecipe), Project (hx, pgo), Demo, Data

This tutorial demonstrates the steps to quantify porosity distribution and the distance to surface analysis.

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Parameter optimization for auto-thresholding a data

Parameter optimization for auto-thresholding a data

Python (pyscro), Project (hx, pgo), Data

In this pyscro example we use opencv and matplotlib to automatically calculate an optimized parameter for thresholding the data based on various metrics.

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How to use PyQt5 in a pyscro module

How to use PyQt5 in a pyscro module

Python (pyscro)

This example shows how to use PyQt5 in a pyscro module.

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Load a Numpy array into PerGeos/Amira-Avizo using the Python API

Load a Numpy array into PerGeos/Amira-Avizo using the Python API

Python (pyscro)

This script simply reads and loads numpy arrays in a user specified directory into PerGeos/Amira-Avizo as an image.

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Additive Manufacturing Powder

Additive Manufacturing Powder

Recipe ISP (hxisp,a2d)

Segmentation of the particles located in the foreground of the SEM image.

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BSE SEM denoiser

BSE SEM denoiser

Deep learning model

U-Net model for denoising back-scattered SEM images.

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Au-Ni-Cl Nanoparticles

Au-Ni-Cl Nanoparticles

Recipe ISP (hxisp,a2d)

This recipe segments nanoparticles of different element from an EDS map.

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Porosity in a polymer divider

Porosity in a polymer divider

Recipe ISP (hxisp,a2d), Demo, Data

The recipe detects pores in a polymer divider (SEM image).

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Porosity in a battery separator

Porosity in a battery separator

Recipe ISP (hxisp,a2d), Demo, Data

The recipe classifies the pores in a battery separator (SEM image).

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Getting Started with Deep Learning Training (Membrane Segmentation)

Getting Started with Deep Learning Training (Membrane Segmentation)

Deep learning model, Project (hx, pgo), Data

Model and companion project for the Deep Learning tutorial.

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Convert To Spreadsheet

Convert To Spreadsheet

Python (pyscro)

This module allows the user to convert a scalar/vector/tensor field into a spreadsheet containing the X,Y,Z positions and the values of the field.

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