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Statistical techniques for edge detection in histological images

Basic information
Original title:Statistical techniques for edge detection in histological images
Authors:David Svoboda, Ian Williams, Nicholas Bowring, Elizabeth Guest
Further information
Citation:SVOBODA, David, Ian WILLIAMS, Nicholas BOWRING and Elizabeth GUEST. Statistical techniques for edge detection in histological images. In First International Conference on Computer Vision Theory and Applications. Portugalsko: INSTICC Press, 2006. p. 457-462, 6 pp. ISBN 972-8865-40-6.Export BibTeX
@inproceedings{631545,
author = {Svoboda, David and Williams, Ian and Bowring, Nicholas and Guest, Elizabeth},
address = {Portugalsko},
booktitle = {First International Conference on Computer Vision Theory and Applications},
keywords = {edge detection; statistical tests; image analysis},
language = {eng},
location = {Portugalsko},
isbn = {972-8865-40-6},
pages = {457-462},
publisher = {INSTICC Press},
title = {Statistical techniques for edge detection in histological images},
year = {2006}
}
Original language:English
Field:Use of computers, robotics and its application
Type:Article in Proceedings
Keywords:edge detection; statistical tests; image analysis

A review of the statistical techniques available for performing edge detection on histological images is presented. The tests under review include the Student's T Test, the Fisher test, the Chi Square test, the Kolmogorov Smirnov test, and the Mann Whitney U test. All utilize a novel two sample edge detector to compare the statistical properties of two image regions surrounding a central pixel. The performance of the statistical tests is compared using histological biomedical images on which traditional gradient based techniques are not as successful, therefore giving an overall review of the methods, and results. Comparisons are also made to the more traditional Canny and Sobel, edge detection filters. The results show that in the presence of noise and clutter in histological images both parametric and non-parametric statistical tests compare well robustly extracting edge information on a series images.

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