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K. Coombes, S. Tsavachidis, J. Morris, K. Baggerly, M. Hung and H. Kuerer, “Improved Peak Detection and Quantification of Mass Spectrometry Data Acquired from Surface-Enhanced Laser Desorption and Ionization by Denoising Spectra with the Undecimated Discrete Wavelet Transform,” Proteomics, Vol. 5, No. 16, 2005, pp. 4107-4117. doi:10.1002/pmic.200401261

has been cited by the following article:

• TITLE: Multidimensional Median Filters for Finding Bumps in Chemical Sensor Datasets

  AUTHORS: Jeffrey C. Miecznikowski, Kimberly F. Sellers, William F. Eddy

  KEYWORDS: Bump Hunting; Image Analysis; Spatial Smoothing; Feature Detection; Mathematical Morphology

  JOURNAL NAME: Journal of Sensor Technology, Vol.2 No.1, March 20, 2012

  ABSTRACT: Feature detection in chemical sensors images falls under the general topic of mathematical morphology, where the goal is to detect “image objects” e.g. peaks or spots in an image. Here, we propose a novel method for object detection that can be generalized for a k-dimensional object obtained from an analogous higher-dimensional technology source. Our method is based on the smoothing decomposition, Data = Smooth + Rough, where the “rough” (i.e. residual) object from a k-dimensional cross-shaped smoother provides information for object detection. We demonstrate properties of this procedure with chemical sensor applications from various biological fields, including genetic and proteomic data analysis.