Comments on the paper 'A novel 3D wavelet-based filter forvisualizing features in noisy biological data', by Moss et al.
Moss et al.(2005) describe, in a recent paper, a filter thatthey use to detect lines. We noticed that the wavelet on which thisfilter is based is a difference of uniform filters. This filter is anapproximation to the second derivative operator, which is commonlyimplemented as the Laplace of Gaussian (or Marr-Hildreth) operator (Marr&Hildreth, 1980; Jahne, 2002), Figure 1. We have compared Moss'filter with 1) the Laplace of Gaussian operator, 2) an approximation ofthe Laplace of Gaussian using uniform filters, and 3) a few common noisereduction filters. The Laplace-like operators detect lines by suppressingimage features both larger and smaller than the filter size. The noisereduction filters only suppress image features smaller than the filtersize. By estimating the signal to noise ratio (SNR) and mean squaredifference (MSD) of the filtered results, we found that the filterproposed by Moss et al. does not outperform the Laplace of Gaussianoperator. We also found that for images with extreme noise content, linedetection filters perform better than the noise reduction filters whentrying to enhance line structures. In less extreme cases of noise, thestandard noise reduction filters perform significantly better than boththe Laplace of Gaussian and Moss' filter.
- Research Organization:
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- USDOE Director, Office of Science; National Institutes ofHealth
- DOE Contract Number:
- DE-AC02-05CH11231; NIHRO1 GM7044
- OSTI ID:
- 919499
- Report Number(s):
- LBNL-59972; JMICAR; R&D Project: GHIMAG; BnR: 400412000; TRN: US200822%%369
- Journal Information:
- Journal of Microscopy, Vol. 225, Issue 1; Related Information: Journal Publication Date: 01/2007; ISSN 0022-2720
- Country of Publication:
- United States
- Language:
- English
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