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Evaluation of Digital Image Recognition Methods for Mass Spectrometry Imaging Data Analysis

Analyzing mass spectrometry imaging data can be laborious and time consuming, and as the size and complexity of datasets grow, so does the need for robust automated processing methods. We here present a method for comprehensive, semi-targeted discovery of molecular distributions of interest from mas... Full description

Journal Title: Journal of the American Society for Mass Spectrometry 2018-10-15, Vol.29 (12), p.2467-2470
Main Author: Ekelöf, Måns
Other Authors: Garrard, Kenneth P , Judd, Rika , Rosen, Elias P , Xie, De-Yu , Kashuba, Angela D. M , Muddiman, David C
Format: Electronic Article Electronic Article
Language: English
Subjects:
ALGORITHMS
Analytical Chemistry
ANIMAL TISSUES
Animals
Application Note
Article
beverages
Bioinformatics
Biotechnology
Chemistry
Chemistry and Materials Science
COMPUTERIZED SIMULATION
DATA ANALYSIS
Data sets
Digital imaging
DISTRIBUTION
DRUGS
food
Homeopathy
IMAGE PROCESSING
Image Processing, Computer-Assisted - methods
Image Recognition
Lymph Nodes - chemistry
Lymph Nodes - metabolism
Macaca mulatta
Maraviroc - pharmacokinetics
Mass spectrometry
Mass Spectrometry - methods
Mass Spectrometry Imaging
MASS SPECTROSCOPY
Materia medica and therapeutics
MATHEMATICAL METHODS AND COMPUTING
Metabolites
Methods
Models, Theoretical
Molecular Imaging - methods
Object recognition
Organic Chemistry
Plant Leaves - chemistry
Proteomics
Rankings
Scientific imaging
Similarity
Spectroscopy
SSIM
Therapeutics
Tissue Distribution
Publisher: New York: Springer US
ID: ISSN: 1044-0305
Zum Text:
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title: Evaluation of Digital Image Recognition Methods for Mass Spectrometry Imaging Data Analysis
format: Article
creator:
  • Ekelöf, Måns
  • Garrard, Kenneth P
  • Judd, Rika
  • Rosen, Elias P
  • Xie, De-Yu
  • Kashuba, Angela D. M
  • Muddiman, David C
subjects:
  • ALGORITHMS
  • Analytical Chemistry
  • ANIMAL TISSUES
  • Animals
  • Application Note
  • Article
  • beverages
  • Bioinformatics
  • Biotechnology
  • Chemistry
  • Chemistry and Materials Science
  • COMPUTERIZED SIMULATION
  • DATA ANALYSIS
  • Data sets
  • Digital imaging
  • DISTRIBUTION
  • DRUGS
  • food
  • Homeopathy
  • IMAGE PROCESSING
  • Image Processing, Computer-Assisted - methods
  • Image Recognition
  • Lymph Nodes - chemistry
  • Lymph Nodes - metabolism
  • Macaca mulatta
  • Maraviroc - pharmacokinetics
  • Mass spectrometry
  • Mass Spectrometry - methods
  • Mass Spectrometry Imaging
  • MASS SPECTROSCOPY
  • Materia medica and therapeutics
  • MATHEMATICAL METHODS AND COMPUTING
  • Metabolites
  • Methods
  • Models, Theoretical
  • Molecular Imaging - methods
  • Object recognition
  • Organic Chemistry
  • Plant Leaves - chemistry
  • Proteomics
  • Rankings
  • Scientific imaging
  • Similarity
  • Spectroscopy
  • SSIM
  • Therapeutics
  • Tissue Distribution
ispartof: Journal of the American Society for Mass Spectrometry, 2018-10-15, Vol.29 (12), p.2467-2470
description: Analyzing mass spectrometry imaging data can be laborious and time consuming, and as the size and complexity of datasets grow, so does the need for robust automated processing methods. We here present a method for comprehensive, semi-targeted discovery of molecular distributions of interest from mass spectrometry imaging data, using widely available image similarity scoring algorithms to rank images by spatial correlation. A fast and powerful batch search method using a MATLAB implementation of structural similarity (SSIM) index scoring with a pre-selected reference distribution is demonstrated for two sample imaging datasets, a plant metabolite study using Artemisia annua leaf, and a drug distribution study using maraviroc-dosed macaque tissue. Graphical Abstract ᅟ
language: eng
source:
identifier: ISSN: 1044-0305
fulltext: no_fulltext
issn:
  • 1044-0305
  • 1879-1123
url: Link


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creatorEkelöf, Måns ; Garrard, Kenneth P ; Judd, Rika ; Rosen, Elias P ; Xie, De-Yu ; Kashuba, Angela D. M ; Muddiman, David C
creatorcontribEkelöf, Måns ; Garrard, Kenneth P ; Judd, Rika ; Rosen, Elias P ; Xie, De-Yu ; Kashuba, Angela D. M ; Muddiman, David C
descriptionAnalyzing mass spectrometry imaging data can be laborious and time consuming, and as the size and complexity of datasets grow, so does the need for robust automated processing methods. We here present a method for comprehensive, semi-targeted discovery of molecular distributions of interest from mass spectrometry imaging data, using widely available image similarity scoring algorithms to rank images by spatial correlation. A fast and powerful batch search method using a MATLAB implementation of structural similarity (SSIM) index scoring with a pre-selected reference distribution is demonstrated for two sample imaging datasets, a plant metabolite study using Artemisia annua leaf, and a drug distribution study using maraviroc-dosed macaque tissue. Graphical Abstract ᅟ
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subjectALGORITHMS ; Analytical Chemistry ; ANIMAL TISSUES ; Animals ; Application Note ; Article ; beverages ; Bioinformatics ; Biotechnology ; Chemistry ; Chemistry and Materials Science ; COMPUTERIZED SIMULATION ; DATA ANALYSIS ; Data sets ; Digital imaging ; DISTRIBUTION ; DRUGS ; food ; Homeopathy ; IMAGE PROCESSING ; Image Processing, Computer-Assisted - methods ; Image Recognition ; Lymph Nodes - chemistry ; Lymph Nodes - metabolism ; Macaca mulatta ; Maraviroc - pharmacokinetics ; Mass spectrometry ; Mass Spectrometry - methods ; Mass Spectrometry Imaging ; MASS SPECTROSCOPY ; Materia medica and therapeutics ; MATHEMATICAL METHODS AND COMPUTING ; Metabolites ; Methods ; Models, Theoretical ; Molecular Imaging - methods ; Object recognition ; Organic Chemistry ; Plant Leaves - chemistry ; Proteomics ; Rankings ; Scientific imaging ; Similarity ; Spectroscopy ; SSIM ; Therapeutics ; Tissue Distribution
ispartofJournal of the American Society for Mass Spectrometry, 2018-10-15, Vol.29 (12), p.2467-2470
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15DISTRIBUTION
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17food
18Homeopathy
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20Image Processing, Computer-Assisted - methods
21Image Recognition
22Lymph Nodes - chemistry
23Lymph Nodes - metabolism
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25Maraviroc - pharmacokinetics
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27Mass Spectrometry - methods
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2ANIMAL TISSUES
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7Bioinformatics
8Biotechnology
9Chemistry
10Chemistry and Materials Science
11COMPUTERIZED SIMULATION
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15DISTRIBUTION
16DRUGS
17food
18Homeopathy
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21Image Recognition
22Lymph Nodes - chemistry
23Lymph Nodes - metabolism
24Macaca mulatta
25Maraviroc - pharmacokinetics
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44SSIM
45Therapeutics
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abstractAnalyzing mass spectrometry imaging data can be laborious and time consuming, and as the size and complexity of datasets grow, so does the need for robust automated processing methods. We here present a method for comprehensive, semi-targeted discovery of molecular distributions of interest from mass spectrometry imaging data, using widely available image similarity scoring algorithms to rank images by spatial correlation. A fast and powerful batch search method using a MATLAB implementation of structural similarity (SSIM) index scoring with a pre-selected reference distribution is demonstrated for two sample imaging datasets, a plant metabolite study using Artemisia annua leaf, and a drug distribution study using maraviroc-dosed macaque tissue. Graphical Abstract ᅟ
copNew York
pubSpringer US
pmid30324263
doi10.1007/s13361-018-2073-0
orcididhttps://orcid.org/0000-0003-2216-499X
oafree_for_read