1. Edge-Based Segmentation
Edge-based segmentation is a popular image processing technique that identifies the edges of various objects in a given image. It helps locate features of associated objects in the image using the information from the edges. Edge detection helps strip images of redundant information, reducing their size and facilitating analysis.
Edge-based segmentation algorithms identify edges based on contrast, texture, color, and saturation variations. They can accurately represent the borders of objects in an image using edge chains comprising the individual edges.
2. Threshold-Based Segmentation
The most straightforward technique for segmenting images is thresholding, which divides pixels according to how intense they are in comparison to a predetermined value or threshold. It works well for separating things with more contrast from backgrounds or other objects.
In photos with little noise, the threshold value T can act as a constant. Dynamic thresholds can be used in specific circumstances. Thresholding creates a binary picture by dividing a grayscale image into two parts based on how well they relate to T.
3. Region-Based Segmentation
A region-based segmentation divides a picture into areas having comparable properties. The technique locates each region using a seed point, which is a collection of pixels. After locating the seed points, the algorithm can expand areas by including more pixels or by contracting and combining them with other points.
4. Cluster-Based Segmentation
Unsupervised classification methods called clustering algorithms are used to find hidden information in photos. By highlighting clusters, shadings, and structures, they improve human eyesight. The method separates data pieces and groups comparable elements into clusters, dividing pictures into groups of pixels with similar properties.
5. Watershed Segmentation
In a grayscale picture, watersheds are changes. The elevation (height) of an image is determined by the pixel brightness in watershed segmentation algorithms, which treat pictures as topographic maps. Using this method, the regions between the watershed lines are marked by lines producing ridges and basins. Based on pixel height, it separates pictures into several zones, grouping pixels with the same gray value.
Medical image processing is one of the key use cases for the watershed method. A diagnosis may be aided, for instance, by being able to distinguish between brighter and darker areas in an MRI scan.
