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Research article - Peer-reviewed, 2013

Improving the stochastic watershed

Bernander, Karl B.; Gustavsson, Kenneth; Selig, Bettina; Sintorn, Ida-Maria; Luengo, Cris


The stochastic watershed is an unsupervised segmentation tool recently proposed by Angulo and Jeulin. By repeated application of the seeded watershed with randomly placed markers, a probability density function for object boundaries is created. In a second step, the algorithm then generates a meaningful segmentation of the image using this probability density function. The method performs best when the image contains regions of similar size, since it tends to break up larger regions and merge smaller ones. We propose two simple modifications that greatly improve the properties of the stochastic watershed: (1) add noise to the input image at every iteration, and (2) distribute the markers using a randomly placed grid. The noise strength is a new parameter to be set, but the output of the algorithm is not very sensitive to this value. In return, the output becomes less sensitive to the two parameters of the standard algorithm. The improved algorithm does not break up larger regions, effectively making the algorithm useful for a larger class of segmentation problems. (C) 2013 Elsevier B.V. All rights reserved.


Mathematical morphology; Image segmentation; Random process; Stochastic watershed; Seeded watershed; Uniform grid

Published in

Pattern Recognition Letters
2013, volume: 34, number: 9, pages: 993-1000

Authors' information

Bernander, Karl B.
Uppsala University
Gustavsson, Kenneth
Uppsala University
Selig, Bettina
Swedish University of Agricultural Sciences, Centre for Image Analysis
Sintorn, Ida-Maria
Swedish University of Agricultural Sciences, Centre for Image Analysis
Luengo, Cris
Swedish University of Agricultural Sciences, Centre for Image Analysis

UKÄ Subject classification

Medical Image Processing
Computer Vision and Robotics (Autonomous Systems)

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