Advancing Healthcare with Medical Image Processing

Key takeaways

Artificial intelligence has been a far-fetched goal of computing since the advent of the computer, but new cognitive computing models may be getting us closer than ever.
The application, in general, enables users to interact with MRI, CT, and other forms of medical imaging data in a number of ways, including the development of models using CAD-designed implants and equipment.
Computers will expand our knowledge and talents when they attain the capacity to think like humans.

Introduction #

Artificial intelligence has been a far-fetched goal of computing since the advent of the computer, but new cognitive computing models may be getting us closer than ever.

The results of cognitive computing, a fusion of cognitive science (the study of the human brain and how it functions) with computer technology, will have far-reaching ramifications in our daily lives, healthcare, industry, and more. One such important fields of study is Medical Image Processing which has aided in advancing the healthcare industry.

What is Medical Image Processing? #

Medical image processing is the study and application of three-dimensional image files of the human body, typically obtained from a computed tomography (CT) or magnetic resonance imaging (MRI) scanner, to diagnose diseases, guide medical procedures such as surgery planning, or for research purposes. Radiologists, engineers, and doctors employ medical image processing to better comprehend the anatomy of individual patients or groups of patients.

How Cognitive Computing Has Made It Possible #

Artificial intelligence (AI) and cognitive computing breakthroughs have had a substantial influence on medical imaging in recent years. Deep learning and other advanced AI algorithms have a wide range of applications in medical image processing and analysis, including image reconstruction, tumor detection, feature extraction, segmentation, and classification, as well as wound healing assessment and detecting cardiovascular abnormalities. The Image Processing

System has a vast range of benefits.

What Are The Benefits of Medical Image Processing? #

The main benefit of medical image processing is that it allows for in-depth but non-invasive investigation of internal anatomy. In order to improve patient treatment outcomes, develop better medical equipment and medicine delivery systems, and arrive at more accurate diagnoses, 3D models of the anatomies of interest can be built and investigated. It has become one of the most essential tools for medical advancement in recent years.

The higher image quality, along with better software tools, enables for exact digital restoration of anatomical structures of diverse sizes and properties, such as bone and soft tissues. Measurement, statistical analysis, and the creation of simulation models with correct anatomical geometries, for example, provide for a better understanding of interactions between human anatomy and medical technology.

How Does Medical Image Processing Work? #

The initial stage in medical image processing is to extract raw data from CT or MRI scans and rebuild it into a format that can be used in appropriate software. A common input for image processing is a 3D bitmap of greyscale intensities with a vowel (3D pixels) grid. The strength of signals from proton particles during relaxation and after the application of extremely high magnetic fields determines the greyscale intensity in a CT scan; however, the greyscale intensity in an MRI is determined by the strength of signals from proton particles during relaxation and after the application of extremely high magnetic fields.

Medical users frequently modify the rebuilt image volume to segment out and change specific anatomical regions of interest, such as tissue and bone. At the 2D and 3D levels, users may perform a variety of image processing tasks, including:

Image filters are used to reduce and remove undesirable noise and artefacts.
Cropping and resampling input data to make picture processing easier and faster
Automated approaches based on AI-based machine learning algorithms are being used to detect distinct anatomical areas utilizing segmentation tools.
Using measurement and statistical techniques to quantify various aspects of picture data, such as centerlines
Importing CAD models of implants or medical equipment in order to investigate how they interact with specific anatomies.
Exporting models for physics-based simulation, further design work, or 3D printing actual duplicates of the anatomy in question

Where & When Does Medical Image Processing Fit In The Product Portfolio? #

Simpleware software provides a wide range of medical applications, from fundamental research through multiple clinical processes. The application, in general, enables users to interact with MRI, CT, and other forms of medical imaging data in a number of ways, including the development of models using CAD-designed implants and equipment. Simpleware ScanIP’s capabilities, for example, are used by device engineers to tackle problems like organizing surgical procedures and analyzing the efficacy of alternative implant designs, as well as exporting models for simulation and design.

Cognitive Robotics goes beyond medical image processing #

After medical image data has been processed, Simpleware ScanIP comes with a variety of add-on modules that allow you to do more with it. Customizing steps and automating time-consuming or repetitive tasks are also options. Users in the medical field, for example, can:

For 3D printing, export STL files from processed medical pictures.
For size and location, combine CAD-designed implants with anatomical imaging data.
Create volume meshes for physics simulations using Finite Element and Computational Fluid Dynamics, such as impact or stress and strain.
Continue product development by translating processed picture data into CAD-friendly NURBS and interfacing with popular CAD software.

Putting Medical Image Processing into Practice #

Patient-specific hemodynamic models of difficult aortic dissections, developed at University College London to better comprehend life-threatening vascular illnesses, provide a fantastic example of how medical image processing works recently. To analyze CT scans and create models suitable for CFD analysis using Simpleware software, the following methods were followed:

Aortic dissection CT images are collected from patient-specific instances.
Simpleware ScanIP is used to rebuild patient geometry, which includes noise processing and segmentation of regions of interest such the dissected aorta and branches.
Smoothing procedures are carried out automatically using scripting to reduce pixelation artefacts.
Surface models of the dissected aorta are created and loaded into ANSYS® software for CFD simulations, which include intraluminal pressure and wall shear-stress-based indices.
The outcomes of the simulation provide hemodynamic information that may be utilized to aid future clinical understanding.

Conclusion #

We now have personal digital assistants (such as Sir and Google) on our phones and computers, but they are not genuinely cognitive systems; they have a pre-programmed set of responses and can only respond to a limited number of requests. However, in the not-too-distant future, we will be able to talk to our phones, computers, autos, and smart homes and receive a true, meaningful answer rather than one that has been pre-programmed.

Computers will expand our knowledge and talents when they attain the capacity to think like humans. We are on the verge of entering an era in which computers may augment human intelligence and knowledge in whole new ways, much like the protagonists of science fiction films rely on computers to make accurate forecasts, gather data, and draw conclusions.

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