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Multi-modal registration of retina images

Is it possible to perform combination of fundus images coming from different imaging equipment or technologies and taken from different angles? RSIP Vision apparently can, since we used our expertise in computer vision for ophthalmology to provide software to a client who wanted to combine direct images of the fundus with fluorescein images, which need to be reconstructed before being connected together to form a more detailed image. The end result provides ophthalmologists with detailed images of the retina, ensuring more efficient and accurate patient diagnosis.

3D object reconstruction by fringe projection

Fringe pattern projection for 3D object reconstruction has been around for 3 decades. The method finds its application in reconstructing static and dynamic objects in

Stabilization in fluorescein angiography

One of the most common tests in ophthalmology is fluorescein angiography: fluorescein is injected in the blood and it moves immediately through the blood vessels

Breakthroughs in biomedical imaging

During the last few decades, the field of biomedical imaging was shaken by major breakthroughs, which have completely changed the way physicians can observe imaging data. For

Type 2 interval fuzzy sets in pattern classification

In search for a pattern in an image, a video or a signal, one has to consider several sources of bias, noise and uncertainties. Such

Object feature tracking in natural scenes

Augmented reality (AR), the superposition of virtual graphical models on screen, has been steadily progressing and finds its application in diverse domain from recreational to

Image Categorization and Retrieval with Fisher Vectors

Testing a set of images for similarity has long been a task of image processing computer vision and machine learning. The plethora of tools and

Image Features for Classification

Classification problems in image and signal analysis require, on the algorithmic side, to take into account complex information embedded in the data. Images might contain

Segmentation and tracking of kidney stones

An affected anatomical region can be treated in a selective and non-invasive manner by localized and contact-free methods such as high-intensity focused ultrasound. Treatment by

Flexible medical instruments for endoscopic surgery

Instrument tracking for robotic aided surgery

The success of complex fine-scale surgery depends heavily on surgeons’ agility, state of fatigue and their freedom to manipulate miniature instruments in the target organ. Internal organs

Lesion Detection in CT scan (Hemorrhagic stroke)

Lesion segmentation by random-forest classifiers

Segmentation of lesions in images, such as those obtained from MRI, ultrasound, CT etc, can be viewed as classifying pixels (or voxels, in the 3-D

Gastrointestinal lesion detection with machine learning

Endoscopic examination is performed to disclose the lesion’s biophysical properties and assess its severity according to its physical appearance. Computer vision and machine learning put a large arsenal of techniques at our disposal. Clever utilization of these techniques enables to do just that, that is, translating expert knowledge into a fine-tuned algorithm, specifically designed for the task of GI lesion detection. The highest level of robustness, accuracy, and reproducibility is required, hence automatic methods can perform as a proper alert system for lesion detection.

Tree Detection and Related Applications in Forestry

Using aerial images taken by drone, plane or satellite, RSIP Vision can create forestry image processing and analysis software to efficiently determine: Trees detection Automatic

Forestry Row Detection and Related Applications

RSIP Vision creates forestry image processing and analysis software by using aerial images taken by drone, plane or satellite. Our algorithms enable to efficiently determine:

Bounded Objects Detection and Related Applications in Forestry

Using aerial images taken by drone, plane or satellite, RSIP Vision develops software for image processing and analysis in forestry to efficiently determine: Forest border

Brain Lesion Detection in MRI Images

Individuals diagnosed with central nervous system (CNS) tumors often suffer from disabilities caused by dysfunctional neurological state and deterioration in systemic activity, leading to relative short expected life-span post diagnosis. Automated segmentation of irregular 3D shapes from MRI volumetric data assists oncologists in their prognosis of these lesions. AI-based methods based on deep learning methodologies, together with imaging techniques in brain lesion detection have been demonstrated in numerous applications to perform accurately and robustly to support the physician.

Automatic Lesion Detection in Fundus Images

Diabetic Retinopathy (DR) is an eye disease resulting from long-term diabetic condition. About 80% of long-term diabetic patients suffer from some degree of DR, which

3D Reconstruction for Bone Alignment

Advances in vision-based medical imaging have completely transformed orthopedic surgery planning and operation procedures. A practical example is a high-impact femur fracture, resulting in a noticeable separation into two segments of the femur. Surgery in such cases requires initial alignment of the proximal and distal parts of the broken bone, followed by insertion and securing of a nail. In RSIP Vision’s solution, image features are matched to a pre-designed flexible geometrical model of the bone.

Bone segmentation in orthopedic surgery

Incorporation of new visualization technologies and planning methodologies shortens examination, planning and operation procedures in orthopedic surgery, while retaining the high standard of accuracy that are required in these common practices. Innovative algorithmic techniques, relying on image processing, computer vision and machine learning are increasingly utilized and have gained approval by regulatory bodies such as the FDA, leading to what is better known as Computer Assisted Orthopedic Surgery (CAOS) procedures. This requires accurate bone segmentation, better performed using the method which we recommend.

Detection and quantification of bone cancer

Metastatic bone malignancies arise following prostate cancer (80% of cases, with 3% five-year survival rate), breast cancer (with no cure) or lung cancers (with 11% two-year survival rate). Bone metastases affect more than 400,000 people annually in the United States with frequent occurrences among patients undergoing irradiation and secondary effect to other treatments. Detection of skeletal metastases has a major impact on devising treatment strategies and prognosis. The solution offered by RSIP Vision produces 3D surfaces of bones and other skeletal-related structures to detect and quantify primary and metastatic bone cancer.

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