Image visualisation

Advanced Cell Classifier is a data analyzer program to evaluate cell-based high-content screens and tissue section images developed at the Biological Research Centre, Szeged and FIMM, Helsinki (formerly at ETH Zurich). The basic aim is to provide a very accurate analysis with minimal user interaction using advanced machine learning methods.

ASAP is an open source platform for visualizing, annotating and automatically analyzing whole-slide histopathology images. It consists of several key-components (slide input/output, image processing, viewer) which can be used seperately. It is built on top of several well-developed open source packages like OpenSlide, Qt and OpenCV but also tries to extend them in several meaningful ways.

arivis Vision4D is a modular software for working with multi-channel 2D, 3D and 4D images of almost unlimited size independent of available RAM. Many imaging systems, such as high speed confocal, Light Sheet/ SPIM and 2 Photon systems, can produce a huge amount of multi-channel data, which arivis Vision4D handles without constraints. Terabyte ready arivis Vision4D main functionality: Easy import of most image formats from microsopes as well as biological formats High performance interactive 3D / 4D rendering on standard PCs and laptops with 3D Graphics Support Intuitive tools for stitching and alignment to create large multi-dimensional image stacks Immediate 2D, 3D and 4D visualization, annotation and analysis regardless of image size Creation, import, and export of 4D Iso-surfaces Powerful Analysis Pipeline for 3D /4D image analysis (cell segmentation, tracking, annotation, quantitative measurement and statistics, etc) Semi-automatic/manual segmentation and tracking with interactive Track Editor Easy design and export of 3D / 4D High Resolution Movies Seamless integration of custom workflows via Matlab API and Python scripting Data sharing for collaboration A user friendly software, easy to learn and use for any life scientist

It is a tool to visualize and annotate volume image data of electron microscopy. Users can annotate objects (e.g. neurons) and skeleton structures. It provides the ability to overlaying the image data with user annotations, representing the spatial structure and the connectivity of labeled objects, and displaying a three dimensional model of it. It can be extended by plugins written in python. A similar, web-based implementation is being developed at webknossos.info. Example datasets are also available.

A menu item in ImageJ that allows you to inspect a 3D stack with orthogonal views (XY, XZ, YZ planes). Slicing plane could be interactively moved by dragging crosses. Pedro Almada wrote a plugin to save the current orthogonal views as montage. ## ImageJ Macro Usages [Save orthogonal views | OrthoSaver](http://uic.igc.gulbenkian.pt/macros/OrthoSaver.ijm) >ImageJ's orthogonal viewer for 3D stacks doesn't let you easily save the current orthogonal view. This macro, once installed, lets users create a montage with the currently open orthogonal views and selection guides. To use it, open an image z-stack and open the orthogonal viewer. With the mouse, choose which are the sections of interest to you and without moving the mouse, press F2. You'll get a montage of the currently selected orthogonal view.