Computer Vision
for Medical Imaging
and Healthcare Applications

Examples of computer vision applications in healthcare

Microscopic thin blood images

• Automatic differential blood counting, classification and analysis.
• K-means cluster algorithm based on color image enhancement for cell segmentation.
• Artery Highlighting.
• Blood vessel counting; quantifying arteriole formation.
• Color blood cell image segmentation and recognition.
• Microscopic thin blood images.
• Red blood cells classification using image processing. – 81% accuracy.

Ophthalmology and diabetes

• Deep image mining for diabetic retinopathy screening.
• Deep Learning for Detection of Diabetic Eye Disease – DL-based (developed by Google).

Cancer

• Cancer metastases detection in biopsy images (e.g. breast cancer in biopsies from lymph nodes) – DL-based; by Google, others.
• Automatic feature learning using multi-channel ROI based on deeply structured algorithms for computerized lung cancer diagnosis – DL-based.

AML/ALL – Leukemia

• Classification of acute leukemia using CD markers – SVM-based, 93.89 % accuracy!
• Chronic lymphocytic leukemia cell segmentation from microscopic blood images using the watershed algorithm and optimal thresholding.
• Automated screening system for acute myelogenous leukemia (AML) detection in blood microscopic Images.
• Microscopic image classification for the detection of acute lymphoblastic leukemia (ALL) – DCT-based.
• Detection of leukemia based on morphological contour segmentation.
• Automated leukemia detection in blood microscopic images using statistical texture analysis.
• Fuzzy C Means Detection of Leukemia Based on Morphological Contour Segmentation.

Other miscellaneous examples and applications

• AI supported pose detection and analysis for remote physical therapy and telerehabilitation with no additional sensors and markers
• Batch-invariant color segmentation of histological cancer images.
• Automatic shadow enhancement in intra-vascular ultrasound (IVUS) images.
• Remote noninvasive temperature monitoring system based on infrared imaging.
• 3d visualization services for microscopy imaging and cell biology (da-cons.de).
• Multiple sclerosis: automated lesion changes tracking (MRI based, example: icometrix.com).
• Mole growth tracking, structure and color changes detection (by consumers themselves using specialized app).
• Recording/broadcasting clinical procedures (surgeries) – multiple angles and feeds.
• Gesture-recognition based surgery assistance – for hands-free manipulation of patient scans and other information during surgical procedures (adora-med.com).
• Automated malaria parasite and their stage detection in microscopic blood images.
• Red Blood based disease screening using marker controlled watershed segmentation and post-processing.
• A new detection algorithm based on fuzzy cellular neural networks for white blood cell detection.
• Morphology-based segmentation of bone marrow cell images.
• 3D reconstruction for assisted navigation (bronchoscopy; solution to guide the endoscopist to target peripheral lesions for biopsy and histological analysis).
• Automatic segmentation of kidneys using deep learning for total kidney volume quantification. – DL-based.
• Measuring peripheral vascular reactivity with diffusive optical imaging.
• Counting contacts between health-care workers and patients within hospital rooms.
• Tracking patient movement (esp. in ICU).
• Measuring HC workers protective equipment adherence within hospital rooms.
• Forensics: MRI-based age estimation (based on bone structure).