Multidimensional Median Filters for Finding Bumps in Chemical Sensor Datasets


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.

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J. C. Miecznikowski, K. F. Sellers and W. F. Eddy, "Multidimensional Median Filters for Finding Bumps in Chemical Sensor Datasets," Journal of Sensor Technology, Vol. 2 No. 1, 2012, pp. 23-37. doi: 10.4236/jst.2012.21005.

Conflicts of Interest

The authors declare no conflicts of interest.


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