ramApp, graphical tool for the pre-processing of hyperspectral images
ramApp is an easy-to-use graphical tool for the pre-processing of hyperspectral images, with a focus on maps obtained using Raman spectroscopy. It covers the most-used tools and techniques for the pre-processing of Raman maps: from cropping and smoothing to clustering and baseline estimation and correction. An optional batch-processing module allows for fast, automated and reproducible analysis of many images at once.
Thanks to the user-friendly and interactive steps of ramApp, researchers can easily start exploring and processing data in a fast and intuitive way. Each step provides many options giving the user a powerful and customizable tool, while spectral plots and intensity maps offer a simple and effective way to visualize and fine-tune the results.
ramApp allows researchers and data scientists to train deep-learning algorithms on large datasets to finally recognise particular pathologies or elements of interest within the images under analysis.
Example of ramApp visualization output: The image shows the average intensity of a leukaemic myeloblast captured using a Raman microscope. The plot on the right highlights the effect of the baseline estimation and correction on the spectrum of a pixel (orange dot in the map on the right).