This session will focus on the exploration of MATLAB functions and methodologies for image processing. We will cover aspects like importing images and metadata, as well as saving processed images and csv data files. Techniques such as image filtering, segmentation and morphological operations will be addressed. To promote a comparison with ImageJ, a simple workflow for object counting and properties extraction will be coded, using as input data the Embryos sample image. To exemplify the potential benefits of MATLAB’s integrative environment, we will plot the extracted data and calculate a regression (using MATLAB’s cftool and/or other functions).

A user comes to the Facility: “I’ve got a set of 2 channels 3D images where objects are overlapping. I think the overlap might not be the same from object to object. I would like to quantify the physical overlap and get a map of quantifications”. Your mission: write the appropriate macro, knowing a user might always change her/his mind, and ask for more… Ready to take on the challenge ?

We take an example image data of microtubule binding protein EB1, and will study how to automatically track those signals and how to analyze the tracking results. We use ImageJ for measuring the temporal changes in signal positions, and will feed the tracking results for analyzing their dynamics using Matlab in the following session.

We will cover the theory behind some useful image preprocessing operations such as filtering for image restoration and feature enhancements, illumination compensation and background correction. We will then combine these operations and write a complete image analysis macro including image correction and 2D stitching of images coming from a large multiposition experiment.

In this session we will be covering ImageJ macro task automation for the batch processing of multiple images. Different techniques will be introduced, contrasted and illustrated in the context of practical bioimage analysis applications. Since image preprocessing typically involves the mechanical application of a sequence of fixed, predictable operations it is often interesting to automate it with ImageJ macro language.

ImageJ, for those with GUI knowledge but without scripting knowledge

Biologists and microscope experts acquire image data. Computational scientists design and implement image processing and analysis algorithms Bioimage analysis is a way of integrating these two resources and to come up with numerical interpretations of biological systems. As this is a still rapidly developing field, standardized procedure is poorly established and we have much more room to develop. Even then, we do have some general approaches that we should take. In this introduction, I will clarify some of the key aspects about investigating biological systems numerically via image data.

Matlab is a commercial software for numerical computing and statistical analysis that can be used to process and analyze multidimensional images. Within this session you will get familiar with the Matlab environment and its programming language. Among the topics addressed: matrix manipulation and advanced indexing, access to files, data inspection and basic plotting. This session will provide you the foundations that you need for the following modules, where you will manipulate images, extract measurements, and statistically analyze and visualize results.

In this session, we will implement a simple ImageJ macro to segment and analyze the blood vessel network of a subcutaneous tumor. The analysis is fully performed in 3D, and possible strategies to extract statistics of the network geometry and interactively visualize the results are also discussed and implemented. Segmenting and extracting the geometry of the blood vessel network inside specific subregions of a tumor is a powerful investigation tool: The density of the vascularization and vessel branching points and the thickness of the vessels are for instance crucial age indicators to understand how the structure developed and possibly necrosed. With the help of a simple ImageJ macro these statistics can be extracted and the network 3D rendered with judicious color/transparency to provide insights on its organization.

In this session we will use a 3D multichannel reconstruction of zebrafish larva to explore the visualization capabilities of Matlab. We will start from extracting and inspecting single slices and will continue with combining multiple channels, finally generating a surface rendering for visual colocalization analysis.During the process we will review methods for manipulating multidimensional arrays, including resizing, reshaping and conditional selection.