Computer vision is applied in cases of not only detection but quick and accurate object measurement. A notable example is medical image processing.
When the image is taken, it needs to be handled by a human operator before it reaches a healthcare specialist. Normally, all measurements and tagging are done manually. Computer vision allows companies to automate and significantly speed up the process while ensuring a nearly perfect accuracy.
Computer vision replicates the human visual system and collects information from the image database to define specific attributes. The technology is very versatile and allows for the flexibility of adaptation. Industries that could benefit from image control automation include manufacturing, agriculture, healthcare, retail, automobile production, financial services, etc.
Some use cases take place behind the scenes, and others are more prominent. Potential practical applications for such technology may be: assembly line control, medical scanning, baggage screening, self-checkout, driver-assistance systems, etc.
To narrow the topic down, let’s take a look at radiology. X-ray testing is used to verify the internal structure and integrity of the object. Once the image is obtained, someone or something needs to compare data points according to a given set of specifications. By the end of this process, there should be a conclusion about whether the object deviates from these parameters.
Computer vision can be applied in the context of medical X-ray imaging (for treatment and research, MRI reconstruction, planning, and conducting surgeries). Currently, diagnosing and treating of dentofacial anomalies is done based on manual analysis of X-ray images. That said, the accuracy and reliability of computer vision can intensify this process. For instance, it can prepare the images and mark essential data points for further analysis.
By now, automating radiography image processing has some fundamental drawbacks that should be addressed when developing a solution:
Circling back to the use of computer vision in medical analysis, let’s take an example of human skull X-ray imagery. We can establish the following objectives that technology needs to accomplish:
Our algorithm for computer vision detection is based on high-accuracy neural networks. The model gathers information from the collection of more than 100,000 images, which have been marked manually. This way, the model receives definitively accurate data so that it knows how the learning process should occur.
As part of the image processing, we can perform segmentation into significant features. After the classification and analysis of these features, we can insert specifications that inform the technology on how to spot object deviations. Then a supervised pattern recognition methodology will provide representative images for future uses.
The dataset can include multiple groups of X-ray images, such as facial bones categories, metal objects (artificial implantable devices, dental amalgams, bone replacements), etc.
After implementing our solution, the waiting time for our clients’ services can be reduced from several days to several seconds. These results can be achieved by addressing the majority of tasks that used to be carried out manually but have been fully or partially automated. Also, we are capable of delivering quality, which surpasses the quality of manual processing.
Objects under inspection can have some parts that are undetectable to the naked eye. Also, when performing any daily functions, there is always a possibility of human errors, resulting in less accurate conclusions. To mitigate these issues, we suggest introducing computer vision algorithms into different fields of business with imaging-related activities.
If you compare manual versus automatic processing procedures, the latter one offers benefits of objectivity that surpass manual limitations. Human operators tend to have different approaches and levels of expertise. Our solution, on the other hand, ensures the best possible quality of results.
Also, automation helps companies monetize their efforts with greater efficiency without inputting countless hours for manual sorting and tagging. The time spent on delivering faster results can be redirected to tasks requiring human intervention.
By relying on large datasets, the technology ensures the reproducibility of diagnostic criteria. In other words, you can use a set of characteristics on multiple objects. Also, if there are uncertainties in measurements, you can quickly re-run the test.
Lastly, it is clear that many application directions have been exploited, and other approaches are yet to be implemented. Different industries and areas of business can adopt different principles. Computer vision is a versatile tool for a wide variety of functions, which you can accomplish with our assistance. We will help you implement CV solutions into your business correctly and meet your goals with greater efficiency.