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Hierarchical image segmentation using a watershed scale-space tree

Authors: Mark Fisher; R.V. Aldridge;

Hierarchical image segmentation using a watershed scale-space tree

Abstract

The watershed transformation is a useful morphological segmentation tool which has been used in a variety of grey-scale image processing applications. However, a major problem with the watershed transformation is that it produces a severe over-segmentation due to the great number of minima embedded in the image or its gradient, and therefore it is rarely applied directly to images. In this paper we discuss modifications to the basic watershed transformation that enable watershed scale trees to be produced and we illustrate the approach with some example segmentations taken from a medical image processing application. The mapping between image and scale tree allows the user to overcome the over-segmentation by either setting a global threshold or interactively editing the tree description of the image.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
7
Average
Average
Average
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