Scale-based normalization of spectral data.
Classification of data that arise as signals or images often requires a standardization step so that information extracted from biologically equivalent signals can be quantified for comparison across classes. Differences in global trend, total energy, high-frequency noise and/or local background can arise from variabilities due to instrumentation or conditions during data collection. This article considers some common ways in which such variation is adjusted for and introduces a generalization of the popular "standard normal variate" transformation. Based on a wavelet decomposition this generalization provides increased flexibility for normalizing spectral data affected by local background noise. Examples from three types of spectroscopy data illustrate the method and its properties.
Randolph
Timothy W
TW
Department of Biostatistics, University of Washington, Seattle, WA 98195, USA. trandolp@u.washington.edu
D003936:Diagnosis, Computer-Assisted / Q000379:methods / Q000592:standards; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods / Q000592:standards; D008962:Models, Theoretical; D012016:Reference Values; D015895:Relative Value Scales; D012815:Signal Processing, Computer-Assisted; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; D019265:Spectroscopy, Near-Infrared; D013057:Spectrum Analysis / Q000379:methods* / Q000592:standards*; D013059:Spectrum Analysis, Raman / Q000379:methods / Q000592:standards