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Combining spectral and image information in the analysis of hyperspectral imaging data


Infrared microspectroscopic imaging is a new technique for rapid, label-free and automated diagnosis of various types of cancer. The technique is expected to enter clinical routine analysis in the years coming. The information content in infrared microspectroscopic image data is overwhelming. An infrared microspectroscopic image typically consists of several thousands to several hundred thousands of pixels, with a full infrared spectrum with several thousand frequency readings in every pixel. Today, only chemical information extracted from the spectral domain is used for classification of tissues into healthy tissue and different cancer types. While morphological information is utilized in medical image analysis of histological images without a spectral domain, the morphological information in the analysis of infrared microspectroscopic images is ignored. DeepHyperSpec will combine deep learning methods with multivariate modelling of scattering and absorption in biomedical vibrational spectroscopy in order to develop a new paradigm for the analysis of hyperspectral imaging data. The acquired knowledge and methodology will allow to fully exploit the spectral and the image domain in hyperspectral imaging data and thus substantially increase the precision, interpretability and stability of classification models. The results of DeepHyperSpec will have an impact on other fields employing hyperspectral imaging, such as geospatial hyperspectral imaging and monitoring by satellites and drones. The research will be conducted by the multidisciplinary Biospectroscopy and Data Modelling (BioSpec) Group at the Faculty of Science and Technology/Realtek, NMBU in close collaboration with four internationally renowned research teams.

Project leader: Achim Kohler

Started: 2019

Ends: 2022

Category: Universiteter

Sector: UoH-sektor

Budget: 10146000

Institution: Institutt for matematiske realfag og teknologi

Address: Ås