V. A. Krylov, J. D. B. Nelson. "Line Extraction via Phase Congruency with a Novel Adaptive Scale Selection for Poisson Noisy Medical Images",
V ECCOMAS Thematic Conferences on Computational Vision and Medical Image Processing VipIMAGE 2015,
Computational Vision and Medical Image Processing V, Proc. of VipIMAGE 2015, Taylor and Francis, pp. 101-106, Tenerife (Spain), October 19-21, 2015.
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Abstract
In this paper we address the problem of extracting curvilinear structures from images in the presence of Poisson-distributed noise that is typically found in low-quality, low-contrast data, as well as in numerous medical imaging modalities. A contrast-robust phase congruency estimation method is proposed such that the optimal range of scales is selected in an adaptive manner for each location in the image. The adaptive regime is driven by a statistically well-principled variance test statistic which is based upon a local measure of the dispersion index. Experimental validation confirms that the adaptive scheme delivers superior line extraction results on mammographic imagery when compared to other recent attempts including the non-adaptive phase congruency.
Bibtex
@INPROCEEDINGS{krylovVIPIMAGE15,
author = {V. A. Krylov, and J. D. B. Nelson},
title = {Line Extraction via Phase Congruency with a Novel Adaptive Scale Selection for Poisson Noisy Medical Images},
year = {2015},
pages = {101--106},
booktitle = {Computational Vision and Medical Image Processing V, Proc. of VipIMAGE 2015},
address = {Tenerife, Spain},
publisher = {Taylor & Francis},
note = {ISBN: 978-1-13-802926-2}
}