Lesson plan

Watershed (image processing)

In the study of image processing, a watershed is a transformation defined on a grayscale image. The name refers metaphorically to a geological watershed, or drainage divide, which separates adjacent drainage basins. The watershed transformation treats the image it operates upon like a topographic map, with the brightness of each point representing its height, and finds the lines that run along the tops of ridges. There are different technical definitions of a watershed. In graphs, watershed lines may be defined on the nodes, on the edges, or hybrid lines on both nodes and edges. Watersheds may also be defined in the continuous domain. There are also many different algorithms to compute watersheds. Watershed algorithms are used in image processing primarily for object segmentation purposes, that is, for separating different objects in an image. This allows for counting the objects or for further analysis of the separated objects. Image:Relief of gradient of heart MRI.png|Relief of the gradient magnitude Image:Gradient of MRI heart image.png|Gradient magnitude image Image:Watershed_of_gradient_of_MRI_heart_image.png|Watershed of the gradient Image:Relief view of the watershed of the gradient of an MRI heart image.png|Watershed of the gradient (relief)

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Article summary

In the study of image processing, a watershed is a transformation defined on a grayscale image. The name refers metaphorically to a geological watershed, or drainage divide, which separates adjacent drainage basins. The watershed transformation treats the image it operates upon like a topographic map, with the brightness of each point representing its height, and finds the lines that run along the tops of ridges. There are different technical definitions of a watershed. In graphs, watershed lines may be defined on the nodes, on the edges, or hybrid lines on both nodes and edges. Watersheds may also be defined in the continuous domain. There are also many different algorithms to compute watersheds. Watershed algorithms are used in image processing primarily for object segmentation purposes, that is, for separating different objects in an image. This allows for counting the objects or for further analysis of the separated objects. Image:Relief of gradient of heart MRI.png|Relief of the gradient magnitude Image:Gradient of MRI heart image.png|Gradient magnitude image Image:Watershed_of_gradient_of_MRI_heart_image.png|Watershed of the gradient Image:Relief view of the watershed of the gradient of an MRI heart image.png|Watershed of the gradient (relief)

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Discussion questions

  1. Summarise the article in your own words. Which single claim would you verify first, and why?
  2. Which claim in this article is most likely to be revised as the sources update? How would you check its current status?
  3. If two sources disagreed about one of these facts, what would you need in order to trust one over the other?

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Key claims and bibliography are generated from the same tracked claims and sources as the article page.