Computer vision for medical imaging and healthcare applications
CV enables medical imaging to transform from static old-school readings into intelligent, data-driven insights.
By embedding AI capabilities, in particular image analysis, into daily healthcare workflows, mature providers can increase diagnostic precision and reduce manual interpretation.
CV supports large-scale imaging, predictive diagnostics, mobile imaging, and visualization, thereby helping clinicians detect disease earlier, personalize programs, and make confident decisions.
The modern-day healthcare landscape heavily relies on precision, in particular talking about medical imaging. In the following material, we’ll discuss how implementing artificial intelligence, in particular computer vision, into traditional medical imaging can reshape old routines.
Introducing computer vision for medical imaging: market dynamics
The numbers keep rising:
- The global computer vision in healthcare settings market surpassed $1.92 billion in 2023
- And should go from $2.60 billion in 2024 to about $53.01 billion by 2034 (Precedence Research)
And the key takeaways speak volumes:
- North America has dominated the field with the largest share of 37% in 2023
- Asia Pacific is expected to witness significant growth in the coming years
- By component, the software segment held the highest market share in 2023.
- By application, the imaging segment led in 2023
Applying computer vision in medical imaging
| Healthcare infrastructure | Mass-analyzing, storing, mining, sharing and tracking Everyday documentation |
| Healthcare education & research | Patient education Data visualization for research |
| Medical imaging | The analysis of images to enable computer-aided diagnostics |
| Predictive analytics | The implementation of novel AI/ML/CV algorithms |
| Mobile services | Mobile devices as portable biometric sensors Personal devices for patient-captured health records |
| eHealth materials | Image sharing within established healthcare systems Automated personalized therapy planning for decision-making |
Medical imaging software development for specific business needs
With years of knowledge and experience, we help our clients to level up their medical imaging.
We digitize:
| Basic modalities | Advanced modalities |
| Optical microscopy | Computed tomography (CT/CAT) |
| General X-ray and fluoroscopy | Magnetic resonance (MRI) |
| Mammographic X-ray | Nuclear/molecular imaging |
| Ultrasound, cart-based/portable | Both SPECT and PET |
Other sources:
- Infrared imaging (temperature monitoring)
- Near-infrared spectroscopy (NIRS, OCT)
- Photoacoustic imaging
- Fluorescence-based imaging
- Echocardiography (heart ultrasound basically)
- Endoscopy (visual light modality)
And even consumer photography (smartphones, webcams, other portable personal devices).
Medical imaging software development: a split of algorithms
Old-school medical imaging
The traditional medical imaging is reliant on rule-based, physics-driven methods (for example, edge detection). These techniques have been the foundation for interpreting general X-rays, MRIs, CTs, ultrasound scans.
Though already considered “old-school,” these methods:
- Are fast and transparent
- Work well under high signal-to-noise ratio and a well-defined anatomy
Geometrical image processing
The geometrical image processing can extract shape-based features, including contours or volumes of organs. This includes the segmentation of vessels, soft tissues, or vessels by using various techniques (active contours, region growing, shape models, watershed algorithms).
Our strength in this area lies in precise structure localization and tracking, in particular:
- Tumor volume estimation
- Vascular tree reconstruction
- Structural deformation monitoring
- And other applications
Newer AI/ML/CV techniques
With recent technology advances, the field has expanded even further toward adaptive, data-backed methods. The older, classical techniques (for example, random forests) have evolved into more advanced frameworks – convolutional neural networks (CNNs) and transformers.
These models especially excel when trained on large annotated datasets and showing great promise.
Leveraging deep learning
Deep learning thrives using big data, which allows clinical systems to identify subtle patterns and correlations. From automating tumor grading to detecting diabetic retinopathy or segmenting lung nodules, neural networks are redefining diagnostic support.
We are actively exploring this domain and expanding our toolkit with architectures and pipelines:
- To boost accurate diagnosis and treatment and minimize human error
- All while staying conscious of meeting regulatory requirements
Computer vision medical imaging: current and future applications
Clinical domains
| Oncology | – Breast cancer – Lung cancer – Prostate cancer And other cancer types |
| Cardiology | – Vascular imaging – Artery highlighting |
| Endocrinology | In particular, diabetes management |
| Ophthalmology | Pattern recognition |
| Neuroscience | Lesion detection and tracking |
| Pharmacokinetics | Clinical trials by using imaging biomarkers |
| Laboratory | Laboratory tests by utilizing blood counting, cell analysis, change tracking, and more |
| Others | – Surgery planning and assistance – Healing tracking – Image visualization – Recommendation systems |
And across novel domains:
- Stats-base diseases prediction
- Specific diseases tracking, and more
Trending areas
| AI enabled pose detection and analysis | Accurate assessment and analysis for patients that undergo physical therapy or rehabilitation |
| 3D visualization | Assisted interventions, aided diagnosis and treatment, and more |
| Breast imaging | Cancer prevention in its early stages |
| Chest CTs | – Pain assessment in ED (emergency departments) – Cardiac angiography – Lung screening And other common applications |
| Point-of-care ultrasound | For hand-carried ultrasound devices |
| Emergency care | To modernize imaging equipment |
| Neuro-molecular imaging | Novel radiotracers to detect neurodegenerative conditions |
| Functional neuroimaging | Cognitive neuroscience (PET, fMRI, NIRS, EEG/MEG, and more) |
The more data available, the better.
Computer vision medical imaging: best examples
Key cases
Image processing and computer vision for oncology digitalization
- Metastases detection in analyzed biopsy images
- Feature learning by using structured algorithms for lung cancer diagnosis
Image processing and computer vision for microscopic thin blood images
- Automatic differential blood counting and analysis
- Artery highlighting
- Cell segmentation by using a K-means cluster algorithm
- Vessel counting
- Color blood cell segmentation and recognition
- Red blood cells classification by using image processing (81% accuracy)
Computer vision in healthcare applications – diabetes
- Deep mining for diabetic retinopathy screening
- Deep learning for diabetic eye detection
Computer vision in healthcare applications – leukemia
- Leukemia classification by using CD markers
- Chronic lymphocytic leukemia segmentation from microscopic blood images
- Leukemia detection through morphological contour segmentation
- Automated screening for detecting myelogenous leukemia in microscopic blood Images
Other examples
General opportunities
- A batch-invariant color segmentation of histological cancer images
- An automatic shadow enhancement in intra-vascular ultrasound images
- Infrared-based remote temperature monitoring for noninvasive thermal imaging
- Kidney segmentation by using deep learning for precise volume quantification
- 3D visualization for microscopy
- 3D reconstruction for bronchoscopy
- An MRI-based multiple sclerosis lesion tracking
- An MRI-based age estimation for forensics
- White blood cell detection by using fuzzy cellular neural networks
- Red blood cell screening through marker-controlled watershed segmentation
- Bone marrow cell morphology for detailed cellular analysis
- Malaria detection and disease stage identification in blood smear images
Consumer-facing tools and apps
| AI enabled pose detection and analysis | Accurate assessment and analysis for patients that undergo physical therapy or rehabilitation without sensors or markers |
| Mole tracking | To detect mole changes in size/color/structure by using smartphone cameras |
Clinical workflows and automated staff monitoring
| Interaction monitoring | To observe doctor-patient meetings |
| ICU patient movement monitoring | To detect longer inactivity or falls |
And even equipment adherence.
Clinical procedures and robotic surgery assistance
| Surgery recording and broadcasting | With multiple camera angles and feeds for education and remote surgical support |
| Gesture-recognition-based assistance | For touchless scan navigation during procedures |
How we can help
Abto Software has over ten years of experience in implementing computer vision applications development. With dozens of successfully completed projects – including those for the healthcare industry – our engineers can handle any challenge.
We will be happy to help you with your next big project – work with professional teams.
Our services:
- AI development
- RPA services
- .NET development
- ASP.NET development
- Web development
- Mobile development
- Cloud services
- Custom product software development
Our expertise:
Choose digital imaging processing for medical applications today and change patient outcomes right away.
CLUTCH