Improvement of SIMS image classification by means of wavelet de-noising

Wolkenstein, M.; Hutter, Herbert; Nikolov, SG; Grasserbauer, M.

doi:10.1007/s002160050249

Cited by 17 publications

(5 citation statements)

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“…These include denoising, compression, image coding, feature selection, etc. In the field of chemistry, application of WT in an analytical image processing was also reported. , In these papers, denoising, compression, feature extraction, registration and fusion of the 2-D and 3-D secondary ion mass spectrometry (SIMS) and electron probe microanalysis (EPMA) images were studied, via the 2-D and 3-D wavelet transform.…”

Section: Applications Of Wavelet Transform In Chemistrymentioning

confidence: 99%

Wavelet: A New Trend in Chemistry

2003

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Since 1989, wavelet transform (WT) has attracted much interest of chemists working on signal and image processing, and the WT-based techniques have been successfully applied to the chemical signal processing. This approach has been demonstrated as fast in computation with localization and having quick decay properties, in contrast to the popular methods existing, especially to the fast Fourier transform. More than 370 papers have been published up to the year 2002 which covered applications of WT in various fields of chemistry, including analytical chemistry, chemical physics, and quantum chemistry. In this paper, we report on applications of WT to data compression, data smoothing and denoising, baseline and background correction, resolution of multicomponent overlapping signals, regression and classification, and analytical images processing in analytical chemistry. Through this report we wish to induce greater interest of chemists in WT and to obtain greater benefits from using the WT-based techniques.

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Section: Applications Of Wavelet Transform In Chemistrymentioning

confidence: 99%

Wavelet: A New Trend in Chemistry

2003

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“…It has been shown to aid in peak definition in infrared spectra 30 and ToF-SIMS image analysis and classification. 20,31,32 Wavelet denoising is accomplished by applying a wavelet transform to the image, which decomposes the image signal into multiple levels containing varying degrees of high-to low-frequency information. Components with small wavelet coefficients contain little signal energy; omitting these components can compress and denoise the image signal while retaining the important image content.…”

Section: Introductionmentioning

confidence: 99%

Denoising and multivariate analysis of time‐of‐flight SIMS images

Wickes

Kim

Castner

2003

Surface & Interface Analysis

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Time-of-flight SIMS (ToF-SIMS) imaging offers a modality for simultaneously visualizing the spatial distribution of different surface species. However, the utility of ToF-SIMS datasets may be limited by their large size, degraded mass resolution and low ion counts per pixel. Through denoising and multivariate image analysis, regions of similar chemistries may be differentiated more readily in ToF-SIMS image data. Three established denoising algorithms -down-binning, boxcar and wavelet filtering -were applied to ToF-SIMS images of different surface geometries and chemistries. The effect of these filters on the performance of principal component analysis (PCA) was evaluated in terms of the capture of important chemical image features in the principal component score images, the quality of the principal component score images and the ability of the principal components to explain the chemistries responsible for the image contrast. All filtering methods were found to improve the performance of PCA for all image datasets studied by improving capture of image features and producing principal component score images of higher quality than the unfiltered ion images. The loadings for filtered and unfiltered PCA models described the regions of chemical contrast by identifying peaks defining the regions of different surface chemistry. Down-binning the images to increase pixel size and signal was the most effective technique to improve PCA performance.

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“…Four papers were published by Nikolov et al [66], Hutter et al [67] and Wolkenstein et al [68,69] in the Vienna University of Technology, for wavelet de-noising of secondary ion mass spectroscopy (SIMS) images. SIMS is a type of surface technique for (1) trace analysis, (2) determination of elemental composition, (3) the identity and concentrations of adsorbed species and (4) elemental composition as a function of depth [70].…”

Section: Mass Spectrometrymentioning

confidence: 99%

A Review on Applications of the Wavelet Transform Technique in Spectral Analysis

2015

J Appl Computat Math

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Improvement of SIMS image classification by means of wavelet de-noising

Cited by 17 publications

References 1 publication

Wavelet: A New Trend in Chemistry

Wavelet: A New Trend in Chemistry

Denoising and multivariate analysis of time‐of‐flight SIMS images

A Review on Applications of the Wavelet Transform Technique in Spectral Analysis

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