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Removing cosmic spikes using a hyperspectral upper-bound spectrum method

Applied Spectroscopy

Anthony, Stephen M.; Timlin, Jerilyn A.

Cosmic ray spikes are especially problematic for hyperspectral imaging because of the large number of spikes often present and their negative effects upon subsequent chemometric analysis. Fortunately, while the large number of spectra acquired in a hyperspectral imaging data set increases the probability and number of cosmic spikes observed, the multitude of spectra can also aid in the effective recognition and removal of the cosmic spikes. Zhang and Ben-Amotz were perhaps the first to leverage the additional spatial dimension of hyperspectral data matrices (DM). They integrated principal component analysis (PCA) into the upper bound spectrum method (UBS), resulting in a hybrid method (UBS-DM) for hyperspectral images. Here, we expand upon their use of PCA, recognizing that principal components primarily present in only a few pixels most likely correspond to cosmic spikes. Eliminating the contribution of those principal components in those pixels improves the cosmic spike removal. Both simulated and experimental hyperspectral Raman image data sets are used to test the newly developed UBS-DM-hyperspectral (UBS-DM-HS) method which extends the UBS-DM method by leveraging characteristics of hyperspectral data sets. A comparison is provided between the performance of the UBS-DM-HS method and other methods suitable for despiking hyperspectral images, evaluating both their ability to remove cosmic ray spikes and the extent to which they introduce spectral bias.

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TYPE Journal Article YEAR 2017
ScopusOSTIDOI

Hyperspectral fluorescence microscopy detects autofluorescent factors that can be exploited as a diagnostic method for Candida species differentiation

Journal of Biomedical Optics

Graus, Matthew S.; Neumann, Aaron K.; Timlin, Jerilyn A.

Fungi in the Candida genus are the most common fungal pathogens. They not only cause high morbidity and mortality but can also cost billions of dollars in healthcare. To alleviate this burden, early and accurate identification of Candida species is necessary. However, standard identification procedures can take days and have a large false negative error. The method described in this study takes advantage of hyperspectral confocal fluorescence microscopy, which enables the capability to quickly and accurately identify and characterize the unique autofluorescence spectra from different Candida species with up to 84% accuracy when grown in conditions that closely mimic physiological conditions.

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TYPE Journal Article YEAR 2017
ScopusOSTIDOI

Experimental and Data Analytical Approaches to Automating Multivariate Curve Resolution in the Analysis of Hyperspectral Images

Data Handling in Science and Technology

Haaland, D.M.; Jones, H.D.T.; Timlin, Jerilyn A.

Analyses of hyperspectral images with multivariate curve resolution (MCR) can be enhanced with the use of automated data preprocessing and improved MCR methods described and demonstrated in this chapter. These new approaches serve to greatly reduce or eliminate the need for user input and to increase the success, sensitivity, and accuracy of hyperspectral image analyses. We have pioneered the use of a dark spectral region, which is a general approach that can be introduced into any hyperspectral imaging system, to automatically remove offsets, structured noise, and uninformative pixels from the raw spectral images without the requirement of user input. The dark spectral region can also be used to minimize mixing between MCR-estimated spectral components to further improve the accuracy of the final pure spectra and their corresponding concentration images. The success of these improved preprocessing and MCR analysis methods is demonstrated with both realistically simulated images and experimentally measured spectral images.

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TYPE Book YEAR 2016
ScopusOSTI
Results 51–75 of 263
Results 51–75 of 263