Region-Basis: An Algorithmic Analysis
The analysis of one of AI’s complex algorithms — Region Based Segmentation
Introduction
The evolution of image segmentation has rapidly grown over the years. We are at the point of our society where image segmentation is used in all aspects of a variety of fields, if not all. From the usage within the sciences for specific needs in image projection to the casual use of image segmentation in a daily applicaiton filter on your phone, image segmentation has continuously been used and evolved significantly. Of these algorithms, region-based segmentation is one of the several primary methods of performing image segmentation. Some of the advantages of this method include faster and easier computation, better performance, etc.
In this algorithmic analysis, I will talk specifically of region-based segmentation, its technical overview, ethical reviews, and a direct implementation of the code in action.
Technical Overview
For starters, there is the technical overview of the algorithm. To understand the technical aspect of this algorithm, one must understand exactly what region-based segmentation is, and what a region is. A region can be defined and classified as a group of pixels exhibiting similar properties. These mentioned pixels may carry similarities in the terms of intensity, color, etcetera. This is why region-based segmentation is one of several primary methods of performing image segmentation by creating object boundaries based on some threshold value(s). These aforementioned values are usually based on the calculated mean of color values or the distributions of similar pixels. With that, they form grouped segments amongst one another. The simplest of these approaches is pixel aggregation, which starts with a set of “seed” points and from these grows regions by appending to each seed points those neighboring pixels that have similar properties. Region growing based techniques are better than the edge-based techniques in noisy images where edges are difficult to detect.
Strong domain use cases in which region-based segmentation excels are medical imagery processing, satellite image processing and military operations, surveillance security, and video processing! So many implementations, and just for one algorithm! Regarding the precise implementation this algorithm has on said topics, medical imagery processing uses region-based segmentation to separate color and pixels on their imagery to find tumors in patients.
Along with tumors, region-based processing is used to find veins, heart strains, and much more to assist the medical sciences in isolating a certain factor of their images. One implementation this algorithm has on satellite image processing is that the military uses region-based segmentation to find targets using their satellite and aerial images.
Regarding surveillance security, this algorithm is used to find people in surveillance videos for security firms. For video processing, it is used to summarize videos. That is only a small fragement of the domains this algorithm excels at and what strategies it implements, and already it is in a vast amount of various fields.
Within the technical interview, it is said to include a developmental history the the specific image segmentation. Funnily enough, after much research on a variety of academic sources, I wasn’t able to pinpoint its creator — only that the region-based segmentation algorithm was created in the pre-2000s before the evolution of more algorithms, closer to 1998.
Insights from the Architecture:
To explain more of the mathematical and data-structure concept behind the region based algorithm, you must understand the algorithm at its simplest form. At each pixel in an image, neighboring pixels are compared to a reference pixel value for the region. If the difference is less than or equal to the Difference Threshold, the neighboring pixel is added to the current region. At simplest form, this operator performs connected-components analysis on gray-scale pixel values. For every pixel p(i) in a region, there exists another pixel p(j) in the region such that
abs (I[p(i)]) — I[p(j)] < Threshold
If there are multiple input images (I1…In), the threshold criteria must hold for all input images
(abs (I1[p(i)] — I1[p(j)]) < Threshold)
Such mathematical implementations can be used of this region-based algorithm along with watershed-based segmentation, multi-resolution projections, and shape-based segmentation.
Ethical Review
An ethical review for such a powerful and complex algorithm such as the region-based segmentation must always be reflected upon its applications. Due to the collection of imagery and data analysis, one cant help but be concerned of its ethical analysis on the potential implementation even with a risk of a lack of enough data. With an algorithm making such important analysis and decision such as what is a military target and what is not, there brings the ethical question as to what is considered a safe range for use on this algorithm to be implemented on society’s delicate operations, whether its militaristic, medical, surgical, etc.
Final Thoughts
In conclusion, though region-based segmentation has been around for more than 20 years, this complex algorithm is very much in use to this day. With such convenience of use and so many implementations of use available, region-based segmentation is significant to this day. It’s relatively simple calculations, quick operational speed, and high functionality in in high contrast images, this algorithm will continue to be helpful in the various field it supports.
Sources
Understanding Region Based Segmentation. https://www.vision-systems.com/factory/consumer-packaged-goods/article/16739413/understanding-regionbased-segmentation
A Summary of Image Segmentations. https://docs.google.com/presentation/d/1gxZ0_0A9h_i2REH5911UwpZPJr-hjkHfZ4fZWd4LdIg/present?slide=id.g10f87652fad_0_126
Region Based Segmentation. https://www.di.univr.it/documenti/OccorrenzaIns/matdid/matdid125113.pdf
Image Segmentation Part 2. https://towardsdatascience.com/image-segmentation-part-2-8959b609d268
Regiona and Edge Based Segmentation. https://www.geeksforgeeks.org/region-and-edge-based-segmentaion/