Multivariate Chemical Image Fusion of Vibrational Spectroscopic Imaging Modalities.

Aoife Gowen,Ronan Dorrepaal

doi:10.3390/molecules21070870

Abstract

Chemical image fusion refers to the combination of chemical images from different modalities for improved characterisation of a sample. Challenges associated with existing approaches include: difficulties with imaging the same sample area or having identical pixels across microscopic modalities, lack of prior knowledge of sample composition and lack of knowledge regarding correlation between modalities for a given sample. In addition, the multivariate structure of chemical images is often overlooked when fusion is carried out. We address these challenges by proposing a framework for multivariate chemical image fusion of vibrational spectroscopic imaging modalities, demonstrating the approach for image registration, fusion and resolution enhancement of chemical images obtained with IR and Raman microscopy.

Highlights

Vibrational spectroscopic techniques have become standard in a wide variety of scientific fields, some of the most common being near-infrared (NIR), mid-infrared (MIR) and Raman spectrometry.These techniques have been used to great effect, traditionally researchers relied on average spectroscopic data from a single sample point or, often in the case of materials, from the average spectrum of a material after pulverisation
In this paper we provide a framework for multivariate chemical image (CI) fusionoftocross enable: cross prediction modality image registration; classification performance; investigation of cross correlations; of one modality improved from another and resolution enhancement
In this paper we have presented a new framework for data fusion of chemical images, using the multivariate nature of this data to enable cross modality image registration, improved pixel level classification and improved resolution through the development of multivariate prediction models

Summary

Introduction

Vibrational spectroscopic techniques have become standard in a wide variety of scientific fields, some of the most common being near-infrared (NIR), mid-infrared (MIR) and Raman spectrometry. These techniques have been used to great effect, traditionally researchers relied on average spectroscopic data from a single sample point or, often in the case of materials, from the average spectrum of a material after pulverisation. Where standard spectroscopy acquires chemical information from a single region, chemical imaging collects chemical information over many spatial regions. These regions can be used to form a high resolution matrix image over an area, where each element is an image pixel comprising a spectrum. Such information is used to provide an insight into the properties of foods [1,2], survey the geological and vegetal make up of landscapes [3] or improve synthetic processes through a better understanding of their resulting products [4]

Methods

Discussion

Conclusion