solimember.blogg.se - Imagecast software for healthcare

Measuring HC workers protective equipment adherence within hospital rooms.Counting contacts between health-care workers and patients within hospital rooms.

Measuring peripheral vascular reactivity with diffusive optical imaging.

Automatic segmentation of kidneys using deep learning for total kidney volume quantification.

3D reconstruction for assisted navigation (bronchoscopy solution to guide the endoscopist to target peripheral lesions for biopsy and histological analysis).

Morphology-based segmentation of bone marrow cell images.

A new detection algorithm based on fuzzy cellular neural networks for white blood cell detection.

Red Blood based disease screening using marker controlled watershed segmentation and post-processing.

Automated malaria parasite and their stage detection in microscopic blood images.

Gesture-recognition based surgery assistance – for hands-free manipulation of patient scans and other information during surgical procedures ().

Recording/broadcasting clinical procedures (surgeries) – multiple angles and feeds.

Mole growth tracking, structure and color changes detection (by consumers themselves using specialized app).

Multiple sclerosis: automated lesion changes tracking (MRI based, example: ).

3d visualization services for microscopy imaging and cell biology ().

Remote noninvasive temperature monitoring system based on infrared imaging.

Automatic shadow enhancement in intra-vascular ultrasound (IVUS) images.

Batch-invariant color segmentation of histological cancer images.

SHOW MORE Other miscellaneous examples and applications

Fuzzy C Means Detection of Leukemia Based on Morphological Contour Segmentation.

Automated leukemia detection in blood microscopic images using statistical texture analysis.

Detection of leukemia based on morphological contour segmentation.

Microscopic image classification for the detection of acute lymphoblastic leukemia (ALL) – DCT-based.

Automated screening system for acute myelogenous leukemia (AML) detection in blood microscopic Images.

Chronic lymphocytic leukemia cell segmentation from microscopic blood images using the watershed algorithm and optimal thresholding.

Classification of acute leukemia using CD markers – SVM-based, 93.89 % accuracy!.

Automatic feature learning using multi-channel ROI based on deeply structured algorithms for computerized lung cancer diagnosis – DL-based.

breast cancer in biopsies from lymph nodes) – DL-based by Google, others.

Cancer metastases detection in biopsy images (e.g.

Deep Learning for Detection of Diabetic Eye Disease – DL-based (developed by Google).

Deep image mining for diabetic retinopathy screening.

Red blood cells classification using image processing.

Color blood cell image segmentation and recognition.

Blood vessel counting quantifying arteriole formation.

K-means cluster algorithm based on color image enhancement for cell segmentation.

Automatic differential blood counting, classification and analysis.

Kitchen Furniture & Appliances RecognitionĮxamples of computer vision applications in healthcare Microscopic thin blood images.

Automatic Fruit Counting for Conveyor Systems.

SimpleContacts Applicant Tracking Software.