title: Scale-based normalization of spectral data.
abstract: 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.
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, WA 98195, USA. trandolp@u.washington.edu
authors.forename: Timothy W
authors.initials: TW
authors.lastname: Randolph
mesh_terms: 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