Abstract: There is provided a method, comprising: feeding 2D image(s) of an internal surface of a colon captured by an endoscopic camera, into a machine learning model, wherein the 2D image(s) excludes a depiction of an external measurement tool, wherein the machine learning model is trained on records, each including 2D images of the internal surface of the colon of a respective subject labelled with ground truth labels of respective bounding boxes enclosing respective polyps and at least one of an indication of size and a type of the respective polyp indicating likelihood of developing maligiancy, obtaining a bounding box for a polyp and at least one of an indication of size and type of the polyp, and generating instructions for presenting within the GUI, an overlay of the bounding box over the polyp and the at least one of the indication of size and type of the polyp.

Abstract: There is provided a method, comprising: feeding 2D image(s) of an internal surface of a colon captured by an endoscopic camera, into a machine learning model, wherein the 2D image(s) excludes a depiction of an external measurement tool, wherein the machine learning model is trained on records, each including 2D images of the internal surface of the colon of a respective subject labelled with ground truth labels of respective bounding boxes enclosing respective polyps and at least one of an indication of size and a type of the respective polyp indicating likelihood of developing maligiancy, obtaining a bounding box for a polyp and at least one of an indication of size and type of the polyp, and generating instructions for presenting within the GUI, an overlay of the bounding box over the polyp and the at least one of the indication of size and type of the polyp.

Abstract: An image processing system connected to an endoscope and processing in real-time endoscopic images to identify suspicious tissues such as polyps or cancer. The system applies preprocessing tools to clean the received images and then applies in parallel a plurality of detectors both conventional detectors and models of supervised machine learning-based detectors. A post processing is also applied in order select the regions which are most probable to be suspicious among the detected regions. Frames identified as showing suspicious tissues can be marked on an output video display. Optionally, the size, type and boundaries of the suspected tissue can also be identified and marked.

Abstract: There is provided a method of generating instructions for presenting a graphical user interface (GUI) for dynamically tracking at least one polyp in a plurality of endoscopic images of a colon of a patient, comprising: iterating for the plurality of endoscopic images: tracking a location of a region depicting at least one polyp within the respective endoscopic image relative to at least one previous endoscopic image, when the location of the region is external to the respective endoscopic image: computing a vector from within the respective endoscopic image to the location of the region external to the respective endoscopic image, creating an augmented endoscopic image by augmenting the respective endoscopic image with an indication of the vector, and generating instructions for presenting the augmented endoscopic image within the GUI.

Abstract: There is provided a method of generating instructions for presenting a graphical user interface (GUI) for dynamically tracking at least one polyp in a plurality of endoscopic images of a colon of a patient, comprising: iterating for the plurality of endoscopic images: tracking a location of a region depicting at least one polyp within the respective endoscopic image relative to at least one previous endoscopic image, when the location of the region is external to the respective endoscopic image: computing a vector from within the respective endoscopic image to the location of the region external to the respective endoscopic image, creating an augmented endoscopic image by augmenting the respective endoscopic image with an indication of the vector, and generating instructions for presenting the augmented endoscopic image within the GUI.

Abstract: An image processing system connected to an endoscope and processing in real-time endoscopic images to identify suspicious tissues such as polyps or cancer. The system applies preprocessing tools to clean the received images and then applies in parallel a plurality of detectors both conventional detectors and models of supervised machine learning-based detectors. A post processing is also applied in order select the regions which are most probable to be suspicious among the detected regions. Frames identified as showing suspicious tissues can be marked on an output video display. Optionally, the size, type and boundaries of the suspected tissue can also be identified and marked.

Abstract: An image processing system connected to an endoscope and processing in real-time endoscopic images to identify suspicious tissues such as polyps or cancer. The system applies preprocessing tools to clean the received images and then applies in parallel a plurality of detectors both conventional detectors and models of supervised machine learning-based detectors. A post processing is also applied in order select the regions which are most probable to be suspicious among the detected regions. Frames identified as showing suspicious tissues can be marked on an output video display. Optionally, the size, type and boundaries of the suspected tissue can also be identified and marked.

Abstract: An image processing system connected to an endoscope and processing in real-time endoscopic images to identify suspicious tissues such as polyps or cancer. The system applies preprocessing tools to clean the received images and then applies in parallel a plurality of detectors both conventional detectors and models of supervised machine learning-based detectors. A post processing is also applied in order select the regions which are most probable to be suspicious among the detected regions. Frames identified as showing suspicious tissues can be marked on an output video display. Optionally, the size, type and boundaries of the suspected tissue can also be identified and marked.

Abstract: Embodiments of the present invention improve edge detection in 2-dimensional image data that may be carried out automatically with minimal user involvement. The invention is carried automatically, using an image processing technique that results in generation of a segmented edge contour, which may then be used in 3-dimensional reconstruction and segmentation.

Abstract: A novel process for modification of polyolefines and their copolymers thereof, enables the production of modified PP with minimal deterioration of molecular weight and low gel content PE and their copolymers and with minimal yellowness. The novel compound is comprised of polyolefin resin or a mixture of resins, unsaturated acidic monomer and organic peroxide, together with low volatility polar acrylic co-monomer and low softening point resin, in a continuous reactive reactor The novel process minimizes sublimation of maleic anhydride and improves distribution of monomers and initiator prior to the melting of the polyolefin.

Abstract: A method and apparatus providing good image enhancement for the visually impaired utilizing the “Ullman-Zur enhancement” algorithm. The method and apparatus consists in obtaining an original image, detecting and enhancing the edges and lines of the image by using Balanced Difference of Gaussians to obtain a first processed image, smoothing the original image by using a convolution of the original image with Gaussian, enhancing the contrast of the smoothed image, calculating the intensity average, AC, and the standard deviation of the intensity, SDC, of the chosen region, and stretching the intensity of the smoothed image linearly according to AC, SDC, and some specific rules to obtain a second processed enhanced image. The first processed image is superimposed on the second processed enhanced image to obtain the final enhanced image that is more readily perceived by a visually impaired person.

Abstract: A micro-perimeter technique using a portable computer and a projector, and using two screening methods, multi-resolution static and bi-directional kinetic. The static method, as applied to a static test, starts with relatively low-resolutions, and only the unseen zones are selectively remapped under higher resolutions during successive iterations. The bi-directional kinetic method, as applied to a kinetic test, uses a response to a continuously moving target, and the unknown response delay is compensated for by an algorithm that merges the patient's response to measurements at the same visual location, but with opposite scanning directions. Offline visualization of the mapping results is performed automatically superimposed over the fundus image.