A performance evaluation of statistical tests for edge detection in textured images

• We present an objective performance analysis of statistical edge detection.
• A controlled dataset of images and tests is given and fully analysed.
• We show how statistical tests can outperform traditional edge detection methods.
• We present recommendations for the tests, giving guidelines for their use.
• Non-parametric tests perform best overall, giving improvements over all tests.

This work presents an objective performance analysis of statistical tests for edge detection which are suitable for textured or cluttered images. The tests are subdivided into two-sample parametric and non-parametric tests and are applied using a dual-region based edge detector which analyses local image texture difference. Through a series of experimental tests objective results are presented across a comprehensive dataset of images using a Pixel Correspondence Metric (PCM). The results show that statistical tests can in many cases, outperform the Canny edge detection method giving robust edge detection, accurate edge localisation and improved edge connectivity throughout. A visual comparison of the tests is also presented using representative images taken from typical textured histological data sets. The results conclude that the non-parametric Chi Square (χ2χ2) and Kolmogorov Smirnov (KS) statistical tests are the most robust edge detection tests where image statistical properties cannot be assumed a priori or where intensity changes in the image are nonuniform and that the parametric Difference of Boxes (DoB) test and the Student’s t-test are the most suitable for intensity based edges. Conclusions and recommendations are finally presented contrasting the tests and giving guidelines for their practical use while finally confirming which situations improved edge detection can be expected.

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