Image segmentation by gradient statistics

Kenong Wu; Steven Schreiner; Brent Mittelstadt; Leland Witherspoon

doi:10.1117/12.310885

24 June 1998 Image segmentation by gradient statistics

Kenong Wu, Steven Schreiner, Brent Mittelstadt, Leland Witherspoon

Proceedings Volume 3338, Medical Imaging 1998: Image Processing; (1998) https://doi.org/10.1117/12.310885
Event: Medical Imaging '98, 1998, San Diego, CA, United States

Abstract

This paper introduces a new gradient-based thresholding method for segmenting gray level images. This method first computes the magnitudes of image gradients. It, then, determines a range of threshold candidates from a statistic measure, called average of averaged gradients. Finally, it derives the image threshold from those candidates. The algorithm is fully automatic and does not analyze the shape of the image histogram. Unlike most gradient-based thresholding methods, this approach effectively reduces the influence of noise in both object and background regions to the threshold selection by computing the threshold from an intensity range, which corresponds only to the intensities at the boundary regions between the object and its background. It is more accurate and orders of magnitude faster than a similar approach. The experiments with synthetic images and real medical images are performed. Comparisons between this method and three other gradient- based approaches are conducted.

Citation Download Citation

Kenong Wu, Steven Schreiner, Brent Mittelstadt, and Leland Witherspoon "Image segmentation by gradient statistics", Proc. SPIE 3338, Medical Imaging 1998: Image Processing, (24 June 1998); https://doi.org/10.1117/12.310885

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