Computer vision for medical imaging and healthcare applications

Computer vision for medical imaging and healthcare applications

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:

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 infrastructureMass-analyzing, storing, mining, sharing and tracking 

Everyday documentation
Healthcare education & researchPatient education

Data visualization for research
Medical imagingThe analysis of images to enable computer-aided diagnostics
Predictive analyticsThe implementation of novel AI/ML/CV algorithms
Mobile servicesMobile devices as portable biometric sensors

Personal devices for patient-captured health records
eHealth materialsImage sharing within established healthcare systems

Automated personalized therapy planning for decision-making
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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 modalitiesAdvanced modalities
Optical microscopyComputed tomography (CT/CAT)
General X-ray and fluoroscopyMagnetic resonance (MRI)
Mammographic X-rayNuclear/molecular imaging
Ultrasound, cart-based/portableBoth 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
EndocrinologyIn particular, diabetes management
OphthalmologyPattern recognition
NeuroscienceLesion detection and tracking
PharmacokineticsClinical trials by using imaging biomarkers
LaboratoryLaboratory 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 analysisAccurate assessment and analysis for patients that undergo physical therapy or rehabilitation
3D visualizationAssisted interventions, aided diagnosis and treatment, and more
Breast imagingCancer prevention in its early stages
Chest CTs– Pain assessment in ED (emergency departments)
– Cardiac angiography
– Lung screening
And other common applications
Point-of-care ultrasoundFor hand-carried ultrasound devices
Emergency careTo modernize imaging equipment
Neuro-molecular imagingNovel radiotracers to detect neurodegenerative conditions
Functional neuroimagingCognitive 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 analysisAccurate assessment and analysis for patients that undergo physical therapy or rehabilitation without sensors or markers
Mole trackingTo detect mole changes in size/color/structure by using smartphone cameras

Clinical workflows and automated staff monitoring

Interaction monitoringTo observe doctor-patient meetings 
ICU patient movement monitoringTo detect longer inactivity or falls


And even equipment adherence.

Clinical procedures and robotic surgery assistance

Surgery recording and broadcastingWith multiple camera angles and feeds for education and remote surgical support
Gesture-recognition-based assistanceFor touchless scan navigation during procedures
Human body pose detection
We deliver scalable solutions that enable remote rehabilitation!
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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:

Our expertise:

Choose digital imaging processing for medical applications today and change patient outcomes right away.

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