How do you choose the best seed points for region growing?

1 Why seed points matter

Seed points are the starting points of region growing, and they have a significant impact on the quality and efficiency of the segmentation. If you choose too few seed points, you may miss some regions or create large regions that are not homogeneous. If you choose too many seed points, you may create too many small regions that are not meaningful or waste computational resources. Therefore, you need to balance the trade-off between under-segmentation and over-segmentation, and choose seed points that represent the main features and variations of the image.

2 How to select seed points manually

One way to select seed points is to do it manually, by inspecting the image and choosing pixels that belong to different regions. This method gives you full control over the seed points, but it can be tedious, subjective, and prone to errors. To make it easier, you can use some tools, such as image editors, image viewers, or image processing software, that allow you to zoom in, pan, and mark the seed points on the image. You can also use some guidelines, such as choosing seed points that are far from the edges, corners, or noise, and that have distinct values from their surroundings.

3 How to select seed points automatically

Another way to select seed points is to do it automatically using algorithms or methods that can generate seed points based on certain criteria. This method can save time and effort, but the results may vary depending on the image and the algorithm. Random selection randomly chooses a number of pixels as seed points without any prior knowledge or analysis of the image, but this method may not capture the main features and variations of the image. Thresholding chooses pixels that have values above or below a certain threshold as seed points, but this method may not be effective for images that have gradual or complex changes in values. Clustering groups pixels that have similar values or features into clusters and chooses one pixel from each cluster as a seed point, which can capture the main characteristics and variations of the image. However, this method may require some parameters such as the number of clusters, distance measure, or clustering algorithm which can affect the quality and performance of the segmentation.

4 How to evaluate seed points

After selecting seed points, either manually or automatically, you need to evaluate them to see if they are suitable for region growing. Visual inspection is one way to do this; you can look at the image and the seed points and see if they match your expectations and objectives. Additionally, you can calculate some statistics, such as the mean, standard deviation, or histogram, of the values or features of the seed points and compare them with the statistics of the whole image or the regions. Lastly, you can run the region growing algorithm with the seed points and check if the segmentation is accurate, complete, and meaningful. This will help you determine if the seed points cover all regions of interest, have clear and consistent values, reflect the diversity and variability of the image, and meet your criteria and goals.

5 How to improve seed points

If you are not satisfied with the seed points or the segmentation results, you can try to improve them by modifying, adding, or deleting some seed points. Manual adjustment is an option, allowing you to manually change some seed points and fine-tune them according to your preferences and needs. Alternatively, you can use automatic refinement with algorithms or methods such as gradient descent, genetic algorithms, or active contours to find the optimal seed points that minimize some error or cost function. Lastly, a hybrid approach combining manual and automatic methods is possible with interactive or semi-automatic tools that allow you to modify or confirm the seed points generated by an algorithm, or to provide some guidance or constraints to the algorithm.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?