USE OF IMAGE PROCESSING IN MEDICAL APPLICATIONS
Introduction:
Biomedical images are a fundamental part of medical science, these images are characterized as the images of the human body that assist in the understanding of nature of the human biological system. This digital image is represented by a matrix with rows and columns where any algorithm developed for one dimensionality can be applied to rows and then to columns. These biomedical pictures are of complete organs, organ system and body parts or might be at the molecular level. In understanding and gathering information from these images, the techniques of image processing have the most extreme significance. The way toward making visual representation of the inside segments or interior portions of a body for clinical analysis, medicinal intervention and visual representation of function of a few organs or the tissue is called Medical imaging.
Use Of Image Processing In Medical Applications:
Medical Imaging mainly concentrates on uncovering and revealing internal structures which are hidden by the skin and bones. In addition, it is used to analyze, diagnose, recognize and treat the illness or disease. This technique is particularly useful for the specialists to make laparoscopic surgeries for viewing the interior parts without actually opening the body. X-ray imaging uses CT scanner, Ultrasound and Magnetic Resonance Imaging. In this way, specialists can look at the body’s obscure or hidden third dimension. By using CT Scanner, inside segments can be exposed and diseased areas can be recognized and spotted very easily. While considering MRI, it gets a signal from the magnetic particles of the body and turns its magnetic tune and with the help of Computer, converts scanned data into images of the internal organs.
Medical specialists around the world are flooded with all kinds of data that they have to collect, process, and analyse. As people, they have a limited capacity, and they’re susceptible to fatigue that’s detrimental to both their own health and their ability to help their patients. Medical images make up around 90% of the data in healthcare. The demand for medical imaging is growing, which results in an increasing amount of data that needs to be processed. This fact opens up the possibility to use innovative IT solutions and employ medical image analysis software. Artificial Intelligence solutions can reduce the time patients wait to receive their diagnoses, expand the number of people that can be examined, increase diagnostic accuracy, and help healthcare organisations optimise their operations.
Machine Learning lies at the heart of image analysis technology. No matter which area of medicine (or other industries) we consider, the software has to be properly trained in order to recognise specific anomalies. For AI, the learning process is significantly different than for people. It utilises extremely large image datasets that have to be processed and analysed by the software. The AI solution then detects significant patterns and uses this knowledge to recognise signs of specific medical conditions which can be observed in these images.
It’s important to note that solutions based on Machine Learning can and, in several cases, already have surpassed human doctors in terms of diagnostic accuracy. Of course, a certain level of supervision by trained specialists is always required. The software can struggle at times, especially with images that are flawed, incomplete or simply poor quality. That’s when the experience of a human doctor and the ability to look at an image without converting it to ones and zeros might be necessary.
Application of Image Processing In Medical Field :
Medical image analysis solutions are relevant to many areas of healthcare, including but not limited to neurology, cardiology, orthopaedics, dentistry, and oncology. They can read images produced with the use of an X-ray, MRI, CT, PET, and ultrasounds. As a result, there’s an opportunity for automation and acceleration of tasks that require a long time when performed manually. The software can learn to recognise specific features of these images, thus making the diagnostic process significantly faster and more reliable.
The possibilities of medical image processing software are impressive. AI-based solutions not only detect irregularities and identify potentially dangerous anomalies, but they’re also able to determine whether they’re dealing with cancer or just a benign tumour. In addition to analysing traditional 2D images, the software can also read 3D and so-called 4D images (ones that display changes in time). Obviously, there’s no need to rely purely on AI in the diagnostic process — it can be used to filter out the healthy patients and alert the doctors when their attention is required. The goal is not to replace physicians and other medical professionals, but to take a part of the burden off them and enable them to help more patients.
X-ray
The medical use of X-rays comes down to producing images of the patient’s internal body parts. Some of the most popular conditions that can be detected with this technology include lung diseases and bone fractures. It’s the oldest form of medical imaging, and it’s still frequently used. In 2015, over 22 million X-ray examinations were performed on NHS patients alone. At the same time, there weren’t enough radiologists to quickly analyse all these images, and some patients had to wait more than a month for their diagnoses.
Image analysis software also has its uses in mammography. This medical technique uses X-rays to detect breast cancer. Research in South Korea showed that, with the aid of trained AI models, doctors can identify the disease in cases where it would otherwise be impossible.
Considering the above, IT solutions can go a long way to not only help specialists deal with large volumes of data, but also increase the accuracy of their diagnoses. Moreover, similarly to ultrasound, X-ray examinations are quite common in developing countries, where there is often a limited number of doctors. Image analysis software can provide the radiologists with much-needed support.
CT Scanning
Computed Tomography (CT) is often used to detect tumours, anomalies in blood vessels, abscesses, and multiple other medical conditions. It can produce 2D slices of the patient’s body or even 3D images. This type of patient examination has become significantly more popular in recent years. The large number of produced images makes it possible to assemble extensive training data sets.
At the same time, this presents an opportunity for IT to take over some of the workload and make sure that patients receive their diagnoses faster. Moreover, as shown by researchers at the University of Central Florida, medical image analysis software can very accurately detect specific diseases. In this specific case, scientists created an efficient system that detected small lung tumours which are difficult to notice for radiologists.
PET Scanning
Positron Emission Tomography (PET) is a medical imaging technique to detect cancer, blood flow issues, brain pathologies, and bone formation anomalies. It utilises special radioactive substances called tracers. This substance is injected into the patient’s body and then a special scanner is used to produce the images. As a result, medical professionals can observe the tracer inside the patient’s body and use this information to make a diagnosis.
Unfortunately, the biggest drawback of this method is the exposure of patients to the radioactive tracers. IT solutions can, at least partially, alleviate this issue. With proper training datasets, it’s possible to develop software which will improve the quality of produced images and remove image noise. This may reduce the overall involvement of radioactive substances. At the same time, there’s the possibility of developing software that’s capable of analysing PET images and identifying symptoms of specific diseases.
MRI
Magnetic Resonance Imaging utilises magnetic fields and radio waves to produce images of internal body parts. It’s mostly used to detect soft tissue conditions, such as aneurysms and circulatory system issues. Unlike the techniques discussed earlier, the MRI doesn’t expose the patient to any radiation. In 2016, almost 40 million MRI scans were performed in the United States alone.
Once again, Artificial Intelligence can be used to analyse these images and detect diseases. The researchers at the Osaka City University managed to develop a deep learning solution that allowed for accurate automated detection of cerebral aneurysms based on MR images. This is another area where the use of AI can not only reduce the workload of radiologists but also improve the reliability of their diagnoses.
Furthermore, AI solutions can be used to detect the early stages of Alzheimer’s disease. Researchers at the University of California used historical patient data to train and test a model that analyses brain scans and recognises early signs of Alzheimer’s. The software was able to detect the condition by looking at scans that were taken six years before the patients were diagnosed with the disease.
Ultrasound
Ultrasound imaging is a technology that’s used across multiple industries. In medicine, it can be utilised to produce images and videos of internal organs, muscles, tendons, and blood vessels. It’s also used to track the development of a foetus during pregnancy.
Medical ultrasounds are a common and non-invasive form of examination. The sheer amount of produced data makes it a great area for medical image analysis software. Reading and interpreting these images is a time-consuming task for radiologists. With the use of Machine Learning, AI solutions can take over a large portion of this workload. As shown in the 2018 NCBI research, deep learning software is able to use ultrasound images to identify breast cancer with accuracy that’s comparable to radiologists. This is especially relevant in developing countries that struggle with a shortage of doctors.
Endoscopy:
The genuine significance of Endoscopy is “looking inside” for medical diagnosis. Unlike other techniques, an instrument is used to inspect and check the interior parts of an empty organ or pit of the body. It permits a specialist to glimpse interior human bodies with the aid of a cutting tool that is attached at the end of the endoscope, and the minor procedures can also be performed if necessary. In this way, this surgery is termed as key hole surgery and it leaves a tiny scar on the human body.
In every endoscope, there are a couple of fiber bundles in which one fiber is used for enlightening the inward structure of the organ and another is used to collect the light reflection. The endoscope is an optical instrument in tubular shape to analyze body disorders that cannot be viewed through naked eye. Endoscopy is generally used for various procedures in pregnancy, plastic surgery, orthopedic surgery, endoscopic spinal surgery. Endoscopes are also used in other fields like bomb disposal personnel and FBI for conducting surveillance via tight spaces.
Stereo Endoscope:
The Stereo endoscope incorporates two cameras that are mounted on a solitary laparoscope. Images from these cameras are forwarded to a video screen. From these 2D images, few sorts of presentation strategies are used to recognize stereo pictures. When a genuine image is perceived, the camera transmits images periodically. This gives the impression of a 3D perspective of the image. Stereoscopic technology aids the medical and clinical field in improving the accuracy of surgery and operational requirements with reduction in operation time and assures patient safety with the help of realistic depth perception of 2D imaging technology. Stereoscopic imaging is also applied to various other fields like digital mammography, teaching of anatomy, diabetic retinopathy, and non invasive surgical operations.
Electrocardiography (ECG):
ECG records electrical activity of the heart over a specific period of time with the help of electrodes fixed on the chest and limbs of the body. These electrodes identifies electrical changes on the skin that occur for every heartbeat. The general objective of ECG is to acquire information about the structure and functioning of the heartbeat. A few signs for performing electrocardiography incorporate suspected pulmonary embolism, fainting or collapse, seizures, cardiac stress testing, atrial flutter. Frequent ECG monitoring is used to check critically sick patients, patients experiencing general anesthesia and patients who have occasionally occurring cardiovascular disorders.
Digital Image Processing Advantages In Medical Applications:
Digital data is non changeable and always retains its originality, irrespective of how many times the data is reproduced. Digital processing is a powerful tool to the doctors that moderate the search for representative images. Once the image is acquired then immediately it displays. Physicians can easily interpret the enhanced/intensified images. It quantifies the changes over time. Quick comparisons of images can be done.
Summary
Medical images make up for a vast majority of data that needs to be processed and analysed in the healthcare industry. The number of images generated worldwide increases every year and this trend is unlikely to change, as more and more people will gain access to better medical care. Radiologists are struggling to keep up with large volumes of data, as, even in the wealthiest countries, their workforce is growing significantly slower than the demand for such services.
This is where digital solutions can come to the rescue. Using Machine Learning algorithms and large training datasets, medical image analysis software is able to accurately recognise symptoms of specific conditions. These types of diagnostic tools can take over some of the more time-consuming tasks and enable doctors to focus on problems that require their direct attention. As such, the smart use of AI solutions can help healthcare organisations provide better and more time-efficient care.
