What is the watershed algorithm?

Image segmentation is accomplished by applying the watershed algorithm, a computer vision and image processing method.

Image segmentation entails splitting an image into several segments or regions according to predetermined criteria to make it easier to understand or more useful for further analysis. The watershed algorithm is very helpful for segmenting images in which objects are in close contact or overlap.

It draws ideas from the hydrological idea of a watershed, in which ridgesRidges refer to the high points or boundaries in an image, analogous to the topographical ridges in a landscape that separate different drainage basins. In image processing, ridges are areas of higher intensity or elevation that act as natural boundaries between different segments of the image. These ridges are crucial because they prevent the merging of adjacent regions during the segmentation process, effectively defining where one segment ends and another begins. serve as borders between several drainage basinsBasins are the low points or valleys in the intensity landscape of an image. They represent areas of minimum intensity that would "collect water" if the image were to be flooded, similar to how a basin collects water in a natural landscape. In the Watershed Algorithm, these basins correspond to regions that will be grouped together as part of the same segment. As the algorithm simulates the process of flooding from these low points, the rising water fills these basins, forming distinct regions that grow until they reach the ridges.. Similar to this, an approach in image processing simulates the flooding of an image from its minima (or low points) in order to determine the catchment basinsCatchment basins are areas where water or other substances collect and pool, surrounded by higher elevations or intensities..

It terms intensity levels as elevations.

Here is a brief overview of the watershed algorithm:

1. Image gradient calculation: Calculate the image's gradient to find possible areas where items are separated.

2. Marker selection: Choose markers from inside the image, usually by using defined criteria or human input. These indicators act as initiators or seeds to initiate the flooding process.

3. Flood simulation: Consider the intensities of the pixels as elevations and simulate a flooding processIt is a technique used to simulate how water or some other substance might spread out from a starting point. beginning at the markers. Catchment basins will form when adjacent pixels that belong to the same item are flooded together.

4. Labeling: Label the catchment basins to differentiate between several divided regions. This labelling procedure results in an image divided so that each area represents a different object.

5. Post-processing: It is possible to refine the segmentation findings and remove tiny or undesirable sections by applying additional post-processing processes.

Below are the sequential outputs of the watershed algorithm's steps:

Types of watershed algorithm

Here is a brief description of the types of watershed algorithm:

1. Morphological watershed: Based on mathematical morphology operations, this approach uses the morphological reconstruction to guide the watershed segmentation. It is beneficial in cases where over-segmentation is a concern.

2. Flooding-based watershed: This method simulates the flooding of the image by incrementally filling basins until they merge. It helps to control the merging of adjacent regions, preventing over-segmentation.

3. Region-based watershed: In this variant, regions are considered the basic elements instead of pixels. It helps handle irregularly shaped objects and is often used in medical image analysis.

Applications of the watershed algorithm

The applications of the watershed algorithms are as follows:

• Image segmentation:

  • It helps separate different objects or regions within an image for better recognition and analysis.

  • Used to segment anatomical structures, such as organs or tumors, from MRI or CT scans.

• Topographic analysis:

  • Assists in identifying drainage basins, watersheds, and terrain features in geographic and environmental studies.

  • Helps in modeling flood-prone areas by analyzing elevation data.

• Pattern recognition:

  • Used to segment textures or patterns in images, aiding in various applications like material analysis or quality control.

• Image processing:

  • It helps differentiate foreground objects from the background in video surveillance and motion detection.

• Biological image analysis:

  • It assists in counting and classifying cells in microscopy images, which is helpful in research and diagnostic applications.

Test yourself

Before moving on to the conclusion, test your understanding:

Conclusion

Regarding segmenting images, the watershed algorithm is a flexible technique that can handle intricate situations involving objects that are near together or overlap. Even though it can be pretty effective, over-segmentation must be prevented through careful marker selection and control systems.

Because of its several versions tailored to specific requirements, the watershed method is flexible enough to fit a broad range of computer vision and image processing applications.