PMID- 17335898
OWN - NLM
STAT- MEDLINE
DCOM- 20070618
LR  - 20181113
IS  - 0142-9612 (Print)
IS  - 0142-9612 (Linking)
VI  - 28
IP  - 15
DP  - 2007 May
TI  - Multivariate analysis strategies for processing ToF-SIMS images of biomaterials.
PG  - 2412-23
AB  - Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a hyperspectral 
      imaging technique. Each pixel in a two-dimensional ToF-SIMS image (or each voxel 
      in a three-dimensional (3-D) ToF-SIMS image) contains a full mass spectrum. Thus, 
      multivariate analysis methods are being increasingly used to process biomaterial 
      ToF-SIMS images so the maximum amount of information can be extracted from the 
      images. This study examines the use of principal component analysis (PCA) and 
      maximum autocorrelation factors (MAF) on four different ToF-SIMS images. These 
      images were selected because they represent significant challenges for biomedical 
      ToF-SIMS image processing (topographical features, low count rates, surface 
      contaminants, etc.). With PCA four different types of scaling methods (auto, root 
      mean, filter, and shift variance scaling) were used. The effect of two 
      preprocessing methods (normalization and mean centering) was also examined for 
      both PCA and MAF. The more computational intense MAF provided the best results 
      for all the images investigated in this study, doing the best job of reducing the 
      number of variables required to describe the image, enhancing image contrast and 
      recovering key spectral features. MAF was particularly good at identifying subtle 
      features that were often lost in PCA and impossible to visualize in single peak 
      images. However, the combination of PCA with either root mean or shift variance 
      scaling provided similar results to MAF. Thus, these combinations offer promising 
      alternatives to MAF for working with large data sets encountered in 3-D imaging. 
      Also, the new method of filter scaling is promising for processing low count rate 
      images with salt and pepper noise. Normalization proved an important tool for 
      deconvoluting chemical effects from topographic and/or matrix effects. Mean 
      centering aided in reducing the dimensionality of the data, but in one case 
      resulted in a loss of information.
FAU - Tyler, Bonnie J
AU  - Tyler BJ
AD  - National ESCA and Surface Analysis Center for Biomedical Problems, University of 
      Washington, Seattle, Washington 98195-1750, USA.
FAU - Rayal, Gaurav
AU  - Rayal G
FAU - Castner, David G
AU  - Castner DG
LA  - eng
GR  - P41 EB002027/EB/NIBIB NIH HHS/United States
GR  - EB-002027/EB/NIBIB NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
DEP - 20070209
PL  - Netherlands
TA  - Biomaterials
JT  - Biomaterials
JID - 8100316
RN  - 0 (Aerosols)
RN  - 0 (Biocompatible Materials)
RN  - 0 (Polystyrenes)
RN  - 0 (Serum Albumin)
RN  - 0 (Sulfhydryl Compounds)
RN  - 112-55-0 (dodecylmercaptan)
RN  - 7440-57-5 (Gold)
SB  - IM
MH  - Aerosols/chemistry
MH  - Algorithms
MH  - Biocompatible Materials/*chemistry
MH  - Factor Analysis, Statistical
MH  - Gold/chemistry
MH  - Humans
MH  - Image Processing, Computer-Assisted/*methods
MH  - Microspheres
MH  - Multivariate Analysis
MH  - Polystyrenes/chemistry
MH  - Principal Component Analysis
MH  - Serum Albumin/chemistry
MH  - Spectrometry, Mass, Secondary Ion/*methods
MH  - Sulfhydryl Compounds/chemistry
MH  - Surface Properties
PMC - PMC1989146
MID - NIHMS20589
EDAT- 2007/03/06 09:00
MHDA- 2007/06/19 09:00
PMCR- 2008/05/01
CRDT- 2007/03/06 09:00
PHST- 2006/10/08 00:00 [received]
PHST- 2007/02/01 00:00 [accepted]
PHST- 2007/03/06 09:00 [pubmed]
PHST- 2007/06/19 09:00 [medline]
PHST- 2007/03/06 09:00 [entrez]
PHST- 2008/05/01 00:00 [pmc-release]
AID - S0142-9612(07)00133-0 [pii]
AID - 10.1016/j.biomaterials.2007.02.002 [doi]
PST - ppublish
SO  - Biomaterials. 2007 May;28(15):2412-23. doi: 10.1016/j.biomaterials.2007.02.002. 
      Epub 2007 Feb 9.