ImageM: a user-friendly interface for the processing of multi-dimensional images with Matlab

Implementation

ImageM strongly relies on Matlab’s Object-Oriented Programming. The core feature is an “Image” class that handles arrays up to five dimensions, corresponding to three spatial dimensions, the channels, and the time. It also encapsulates image meta-data such as spatial calibration, image name, or channel names.

The user interface relies on various image viewers adapted to the type (grayscale, color, label, multivariate) and the dimensionality of the image (Figure 1a). Interactive tools allow for quick image exploration, for example histograms, line profiles and 3D visualization. The architecture was kept modular to facilitate the inclusion of new features.

Figure 1. Result of a typical image processing workflow with ImageM.

Original 3D image is visualized via adapted viewer (a). Image processing operators are applied (b), using contextual dialogs (c). Segmentation results are represented by label images (d,g). Analysis of the image results in table frame (f). Menu actions are transcripted into runnable Matlab commands (e). Original image courtesy of K. Belcram (Moukhtar et al., 2019).

Operation

To have the whole functionalities, Matlab (v2020a) is required, as well as the Image Processing and the Statistics toolboxes. The Gui Layout Toolbox (Sampson, 2021) is required to benefit from flexible image viewers. ImageM runs on a typical workstation. The application is launched by typing “ImageM” from the command line.

Menus provide quick access to common image processing operators, organized in a way to make them easily discoverable by a non-expert user. When necessary, intuitive dialogs allow for setting up parameters. Preview of the results facilitates the choice of relevant settings. Region of interest may be defined interactively to perform interactive measurements.

To facilitate usage during scripting and integration with custom developments, images can be easily imported from and exported to the workspace. Applying an operator results in a log entry that can be integrated into a script for quickly generating a workflow from the succession of operations.

Segmentation and analysis of regions

ImageM integrates a large family of image processing operators (linear filtering, mathematical morphology, segmentation). Intuitive dialogs allow tuning of parameters and preview of the result, facilitating the choice of relevant settings (Figure 1c). The type of the result image is automatically inferred to provide the most adequate visualization (Figure 1d and e).

Segmentation algorithms result in binary or label images that can be used for quantitative analysis of regions. The spatial calibration of images is considered when available. Features can be visualized as geometric overlays on the image or exported to data tables (Figure 1f).

Multivariate image analysis

Multi-channel images or microscopy images contain pixels represented of several values. Multivariate image analysis provides a convenient formalism to analyze such images by taking account both the spatial and the spectral dimensions (Geladi & Grahn, 2006). ImageM can convert multi-variate images into data tables, allowing their exploration through multi-variate analysis tools such as Principal Component Analysis or k-means clustering (Figure 2). Resulting tables can be easily back-converted to images to facilitate the visual interpretation.

Figure 2. Multivariate image analysis of a macrofluorescence image.

Left panel: original multivariate image (from Devaux, 2008). Middle panel: application of K-means clustering on principal components. Right panel: color representation of k-means classes.

Octave

ImageM can also run within the open-source platform, Octave, after some modification of the code (see Extended data (Legland & Devaux, 2021)).

Use case

We present here a use case describing image segmentation and region analysis of the 3D image presented in Figure 1. The Extended data (Legland & Devaux, 2021) contains the sample files, and more detailed use cases.

Underlying data

All data underlying the results are available as part of the article and no additional source data are required.

Extended data

Zenodo: Extended data for the manuscript “ImageM: a user-friendly interface for the processing of multi-dimensional images with Matlab”, http://doi.org/10.5281/zenodo.4705240 (Legland & Devaux, 2021).

This project contains the following extended data:

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).