2023 article
Development of an object-based image analysis tool for mass spectrometry imaging ion classification
Eisenberg, S. M., Knizner, K. T., & Muddiman, D. C. (2023, May 24). ANALYTICAL AND BIOANALYTICAL CHEMISTRY.
author keywords: IR-MALDESI; Object-based image analysis; Mass spectrometry imaging; Image segmentation; Image classification; MATLAB
TL;DR:
The utility of an ion classification tool (ICT) developed using object-based image analysis in MATLAB is presented, which was able to accurately classify 45/50 ions as on-tissue or background.
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UN Sustainable Development Goal Categories
3. Good Health and Well-being
(Web of Science)
Source: Web Of Science
Added: July 3, 2023
Mass spectrometry imaging (MSI) is an analytical technique that can detect and visualize thousands of m/z values resolved in two- and three-dimensional space. These m/z values lead to hundreds of molecular annotations, including on-tissue and background ions. Discrimination of sample-related analytes from ambient ions conventionally involves manual investigation of each ion heatmap, which requires significant researcher time and effort (for a single tissue image, it can take an hour to determine on-tissue and off-tissue species). Moreover, manual investigation lends itself to subjectivity. Herein, we present the utility of an ion classification tool (ICT) developed using object-based image analysis in MATLAB. The ICT functions by segmenting ion heatmap images into on-tissue and off-tissue objects through binary conversion. The binary images are analyzed and within seconds used to classify the ions as on-tissue or background using a binning approach based on the number of detected objects. In a representative dataset with 50 randomly selected annotations, the ICT was able to accurately classify 45/50 ions as on-tissue or background.