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.

Automatic extraction of surface cross section
Python (pyscro), Project (hx, pgo)
20 Oct 2020 - Create partially transparent capping surfaces

Cell Detection and Tracking 2020.2
Recipe ISP (hxisp,a2d), Python (pyscro), Project (hx, pgo), Demo
21 Aug 2020 - Simple workflow for tracking cell nuclei in a 3D Time Series.

Multi-Scale Cylinder Correlation
Python (pyscro), Project (hx, pgo)
30 Jul 2020 - This module gives better results for fiber-tracing in the case of multiple fibers with differing or varying diameters in the same dataset.

Segment TEM data without artifact and background effect
Recipe (hxrecipe), Tcl, Project (hx, pgo)
10 Jul 2020 - Segment TEM data aided by the Structure enhancement filter.

Molar Segmentation Using Watershed and Top-Hat and Surface Generation
Project (hx, pgo), Demo, Data
11 May 2020 - Data, tutorial, and complete project for segmenting a CT-scanned human molar artifact. The tutorial and project cover advanced image segmentation and surface generation.

Porosity Clustering Analysis and Distance to Surface
Recipe (hxrecipe), Project (hx, pgo), Demo, Data
21 Oct 2019 - This tutorial demonstrates the steps to quantify porosity distribution and the distance to surface analysis.

Parameter optimization for auto-thresholding a data
Python (pyscro), Project (hx, pgo), Data
7 Oct 2019 - In this pyscro example we use opencv and matplotlib to automatically calculate an optimized parameter for thresholding the data based on various metrics.

Getting Started with Deep Learning Training (Membrane Segmentation)
Deep learning model, Project (hx, pgo), Data
27 Jun 2019 - Model and companion project for the Deep Learning tutorial.